Air Service and Urban Growth:Evidence from a Quasi-Natural Policy Experiment∗

Bruce A. Blonigen† Anca D. Cristea‡

University of Oregon and NBER University of Oregon

January 2015

Abstract

While significant work has been done to examine the determinants of regional devel-opment, there is little evidence on the role of air services. This paper exploits thelarge and swift changes to air traffic induced by the 1978 Airline Deregulation Act toidentify the link between air traffic and local economic growth. Using data for 263Metropolitan Statistical Areas (MSAs) over a two-decade time period, we estimate theeffects of airline traffic on local population, income, and employment growth. Our mostconservative estimates suggest that a 50-percent increase in an average city’s air trafficgrowth rate generates an additional stream of income over a 20-year period equal to7.4 percent of real GDP, the equivalent of $523.3 million in 1978 dollars.

JEL: O18, R1, R4Keywords: air transport, passenger aviation, urban growth, regional development, Air-line Deregulation Act

∗We thank the editor and the anonymous referees for their constructive comments, which significantlyimproved this paper. We also thank Leah Brooks, Ariel Burstein, Jan Brueckner, Pablo Fajgelbaum, JamesHarrigan, Walker Hanlon, Vernon Henderson, David Hummels, Jason Lindo, Jerry Nickelsberg, NicholasSheard, Matthew Turner, Alex Vrtiska, Glen Waddell Wes Wilson and Clifford Winston for helpful dis-cussions and comments, as well as seminar participants at UCLA and the Urban Economic Associationmeeting (Ottawa, 2012) for useful suggestions. Mitchell Johnson provided excellent research assistance. Anyremaining errors are our own.†Department of Economics, University of Oregon, 1285 University of Oregon, Eugene, OR 97403, USA.

Ph: (541) 346-4680. E-mail: bruceb@uoregon.edu.‡Corresponding Author: Department of Economics, University of Oregon, 1285 University of Oregon,

Eugene, OR 97403, USA. E-mail: cristea@uoregon.edu.

1 Introduction

Almost since the invention of the airplane, policymakers at all levels of government have

spent considerable resources to promote air services for their constituents. Currently in

the United States, local airports and communities are quite active in providing subsidies and

pledging future travel tickets in order to encourage airlines to add new routes for their region

(e.g., Tampa1), or to deter them from terminating strategic routes (e.g., Portland2), or from

downgrading a city from its hub status (e.g., Cleveland3). A 2009 survey by Airports Council

International North America found that of the 52 responding airports, 33 had incentive

agreements involving domestic air service, and 23 airports had incentive agreements for

international air service.4

The universal justification for these government policies is the stated belief that air

services are crucial for regional economic growth. In support of this belief there is anecdotal

evidence suggesting that air transport improves business operations by providing quick access

to input supplies, it stimulates innovation by facilitating face-to-face meetings, and overall it

represents an essential input to the activity of many industries. However, it is not clear how

much local economies significantly rely on air services, nor the extent to which other modes

of transportation and communication can be easily used as substitute. In the end, a positive

correlation between air services and economic growth may make policymakers erroneously

believe that there is a causal relationship that they can affect, when other factors out of

their control could be driving the positive correlation.

Estimating the economic benefits of transportation projects in general, and of air ser-

vices in particular, is difficult because there is a strong interdependence between the provision

of such services and regional growth. Communities that benefit from more rapid economic

growth tend to also invest more in infrastructure, and in the provision of transportation ser-

vices. In turn, the availability of reliable transportation services further stimulates regional

development.

Perhaps due to the difficulty of identification, there are only a couple prior studies

examining the effect of air service on regional growth. Brueckner (2003) estimates the effect

of airline traffic on employment using cross-sectional data at the Metropolitan Statistical

Area (MSA) level, and finds that a 10 percent increase in passenger enplanements leads to

an approximately 1 percent increase in MSA employment, with service sectors responsible

1See “Intense competition boosts airport incentives to airlines” at: http://www.tampabay.com/news/business/airlines/intense-competition-boosts-airport-incentives-to-airlines/1042035

2See “Port’s gamble on Delta pays off” from 06/11/2010 on www.oregonlive.com.3See “Pittsburgh could foreshadow future of Cleveland Hopkins International Airport” from 11/22/2009

on www.cleveland.com4McAllister, Brad, ”Regaining stability,” Airport Business Magazine, september 15, 2011 at:

http://www.airportbusiness.com/print/Airport-Business-Magazine/Regaining-stability/137314

1

for most of the effect. Brueckner (2003) uses hub status of an airport, the MSA’s centrality

within the U.S., and its proximity to the nearest large metropolitan area to instrument for

the endogeneity of the level of air transport services. However, hub status may be endogenous

with current and expected growth of an MSA, while geographic factors may affect general

economic growth of a region just as much as air services.5 Green (2007) estimates the effect of

air transport on regional growth. Using information on 83 MSAs, he regresses air passenger

traffic levels in 1990 on subsequent decennial population and employment growth, and finds

that a 10 percent increase in boardings per capita generates a 3.9 percent higher population

growth and 2.8 percent higher employment growth for the period 1990-2000. However,

economic outcomes such as population, employment, and even air services are persistent

processes. This too makes identification difficult in the absence of a major exogenous and

long-lasting shock to the airline service.

In this paper we present an alternative way to identify the relationship between air

services and regional economic growth, which relies on time series variation. We exploit

a quasi-natural experiment that stems from the dramatic changes in the aviation industry

following the 1978 U.S. Airline Deregulation Act.6 In just a few years following the legislation,

the industry was rapidly deregulated, transitioning from an environment of tight policy

restrictions to free market. This transformation was accompanied by large changes in air

services across cities to unwind the artificial constraints imposed under regulation. These

constraints included a regulatory regime that had stopped approving new routes since the

early 1970s, and that had explicitly subsidized air service to small and medium communities

in the U.S. at the expense of larger cities.7 To illustrate the relevance and impact of the policy,

Figure 1 illustrates the relative changes in air services across city size groups in the wake

of deregulation around the phase-in years 1977-1983. The simple snapshot of the raw data

suggests that the 1978 aviation deregulation led to sizable, long-lasting and heterogeneous

effects on the provision of aviation services across urban locations.

Using historical data on economic and aviation indicators for 263 MSAs spanning

the period 1969-1991, we exploit the significant changes in passenger aviation triggered by

industry deregulation in order to infer the effect of air services on regional growth.8 We take

5There is a significant economic geography literature showing how spatial location influences regionaldevelopment and income growth via market access effects. See, for example, Redding and Venables (2004)for a cross-country analysis, or Hanson (2005) and Head and Mayer (2006) for a regional analysis.

6Using the German division and reunification as a natural experiment, Redding et al. (2011) provideinteresting evidence for how persistent the shocks to the aviation network can be, in large part due to thepresence of sunk costs and network externalities.

7The only major form of regulation in the industry that remained was the Essential Air Service program,which mandated service to very small communities. This program has been quite limited in the communitiesit covers, and currently provides services to approximately 3000 passengers a day.

8Technically, the unit of observation in our dataset is a Core Based Statistical Area (CBSA), whichincludes both micropolitan and metropolitan statistical areas. However, since 75 percent of the cities in our

2

advantage of the variation in the growth of air traffic before versus after the policy change,

and employ a difference-in-differences framework. Given the artificial distortions swiftly

unwound by deregulation, it is plausible that these deviations in air passenger changes over

time were unrelated to urban growth patterns. If so, then an OLS estimation methodology

would be sufficient to causally identify the effect of air passenger service on regional growth.

However, we take a number of additional measures to purge our results of any remaining

endogeneity concerns. First, we estimate our difference-in-differences framework on already

differenced data (i.e., variables expressed as average annual growth rates), so that we identify

our effects from deviations in growth paths.9 Second, we control for location-specific secular

trends, as well as for joint determinants of air traffic and urban growth via city fixed effects

and control variables for initial period economic conditions. Third, we are only identifying

our effects from changes in air passenger traffic that occurred in a relatively short time

window following deregulation (i.e., 1977-1983). Longer time frames would mix these one-

time deregulatory changes with ongoing air passenger changes that are made in response to

current and expected future regional growth trends.

While we believe that these strategies largely eliminate endogeneity concerns, we also

employ instrumental variables methods in tandem with our difference-in-differences frame-

work. We explore a number of alternative instruments to isolate the exogenous component

of the change in the growth of air traffic that occurred in the wake of deregulation. First,

we instrument the deviation in a city’s air traffic growth with the average deviation in air

traffic growth among cities within the same size category as the city in question. Second, we

use information only on “neighboring” cities to construct the average change in air traffic

growth triggered by deregulation, and explore a number of alternative definitions of what

constitutes a neighboring city.

Our analysis provides evidence for a direct effect of air services on regional development

that is robust to these many strategies for identifying this relationship. We find statistically

significant and positive estimates across the OLS and across the IV estimates. For a given

city, a 50 percent increase in the growth rate of air passenger traffic (a relatively small change

in our sample10) leads, on average, to an increase in the annual population growth rate rang-

sample are metropolitan statistical areas, throughout the paper we will be using the abbreviation MSA torefer to cities in our sample.

9This strategy follows the “difference of differences” approach pursued by Trefler (2004).10There is a lot of variation across communities in the change in air traffic growth rates pre- versus

post-deregulation. Rather than rely on the large sample deviation to interpret the coefficients, we choseinstead to use a more moderate value of a 50 percent change in the air traffic growth rate. There are only42 communities (16%) in our sample whose air traffic growth rate changed by 50 percent or less. While thismay not be a very large number, we think this conservative scenario is more representative for out-of-sampleperiods. The kind of changes in air traffic growth rates that happened around the deregulation period areprobably much larger than what we tend to observe today. In fact, the change in air traffic growth rate at

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ing between 1.55 and 4.2 percent. To put this result in perspective, this corresponds to an

additional 0.42 to 1.14 percent increase in the population level of the average city after a

20-year period. In the same manner, we find that a 50 percent increase in a city’s air traffic

growth rate leads to between 1.65 and 3.45 percent increase in the annual income growth

rate (which corresponds to an additional 0.6 - 1.20 percent increase in the level of per-capita

income after a 20-year period), and to 2.7 - 4.7 percent increase in the annual employment

growth rate (which corresponds to an additional 1.6 - 5.7 percent increase in total employ-

ment after a two-decade period) on average. When estimating the employment effects by

sector, we find that services and retail industries are the ones experiencing significant growth.

These effects are comparable in magnitude to similar studies in the literature.11

Our paper contributes to the broader literature on the determinants of urban growth,

among which transportation infrastructure is one. In two related papers, Glaeser, Kallal,

Sheinkman and Shleifer (1992) and Glaeser, Sheinkman and Shleifer (1995) provide a frame-

work for analyzing the determinants of urban growth. While they employ their set-up to

examine the effect of agglomeration economies and human capital on urban growth, we fo-

cus on the role of air services. A study closer to ours is Duranton and Turner (2012), which

examines the causal relation between road transportation and city growth. The study finds

large and significant effects of highway kilometers on employment and population growth,

focusing particularly on intra-city transportation investments as the main determinants of

growth. Using historical data on U.S. railroad construction, Donaldson and Hornbeck (2013)

assess the importance of railroads to the national economy by quantifying the changes in

the agricultural land values resulting from improvements in market access at county level.

Banerjee, Duflo and Qian (2012) and Faber (2013) focus on a rapidly growing developing

country such as China, and investigate empirically how access to transport infrastructure

(e.g., railroads, highways) affects regional economic growth. They find mixed results regard-

ing the local benefits of transport infrastructure investments.

Our paper also complements an expanding literature evaluating other economic conse-

quences of transport infrastructure projects. A prior line of research has examined the ag-

gregate relationship between public spending and economic growth finding mixed results.12

The availability of historical data on road or railroad construction at detailed geographical

level has spurred a number of new studies on the impact of infrastructure, though most of

these papers focus on how infrastructure affects trade between regions rather than regional

the national level between 2011-2012 and 2012-2013 is 43 percent (source: Department of Transportation).11For example, Duranton and Turner (2012) find that a 10 percent increase in a city’s stock of highways

leads to 1.5 percent increase in local employment over a 20-year period.12See Aschauer (1989), Munnell (1992), Holtz-Eakin (1994), Evans and Karras (1994), and Shirley and

Winston (2004). Fernald (1999) and Chandra and Thompson (2000) provide insights into the heterogeneouseffect of transportation infrastructure across industries and counties, which may explain the mixed findings.

4

growth.13

The remainder of our paper proceeds as follows. The next section describes the context

and consequences of the Airline Deregulation Act of 1978. Section 3 lays out the empirical

model and discusses the estimation methodology. The data sources are presented in section

4, while section 5 describes the estimation results. Finally, section 6 concludes.

2 The 1978 Aviation Deregulation and Its Appropri-

ateness as a Quasi-Natural Experiment

2.1 Institutional Background

The 1978 Aviation Deregulation Act (ADA) in the United States was a significant policy

change that led to swift and dramatic transformations in the aviation industry. Important

for our purposes, the evidence suggests that it had a number of features that make it an

appropriate quasi-natural experiment.

First, the regulatory regime had clearly and systematically distorted air service pat-

terns from what one would see in the free market following the industry deregulation. Before

1978, the development and activity of the airline industry was closely overseen by the Civil

Aeronautic Board (CAB). The CAB was in charge of certifying and approving new entrant

carriers, and assigning them precise point-to-point routes that they had to operate at prede-

termined airfares. Except for aircraft capacity and flight frequency, all operation decisions,

such as entry and exit, route allocations, intensity of market competition, and price levels,

were centrally determined by the CAB (Morrison and Winston, 1986). Entry was tightly

controlled, certification being awarded on a per-case basis and only for operations on spec-

ified routes (Bailey et al., 1985). Rather than aim for industry efficiency, CAB regulations

strived to protect the well-being of all existing airlines. It achieved this by suppressing

market competition and favoring cross-route subsidization (Dempsey, 1987; GAO, 1996).14

13Michaels (2008) provides evidence that rural counties with access to interstate highways experience anincrease in trade-related activities such as trucking and retail sales. Duranton, Morrow and Turner (2014)bring additional evidence for the role of interstate highways in determining the specialization of urbanlocations in sectors producing and trading heavy goods. Sheard (2012) uses the 1944 Civil AeronauticsAdministration national airport plan as an instrument for current day airport sizes to examine their impacton the composition of industrial activity. Donaldson (2010) examines how the introduction of railroads inIndia differentially affected incomes and prices across regions.

14In documenting the cross-subsidization practice of the CAB, Eads (1972, pg. 205-6) states that: “themore recent attempt by the Board to internalize the local service subsidy (by giving carriers routes on whichit hoped substantial profits could be made) required that entry controls be continued. It also meant thatthe Board, in deciding which carrier to favor with route awards, had to continue to base its choice in largepart on the need of local service carriers for profit and not on the more desirable criterion of which carrierwould provide the service at the least cost.”

5

At the peak of the government intervention, in the early 1970s, CAB ceased to certify new

carriers entirely, and even rejected requests by existing carriers to enter new city-pair routes

(Borenstein, 1992).

A second important feature of the deregulation was the fast rate at which significant

transformations were taking place, as “policy changes followed one another with dazzling

rapidity” (Bailey et al., 1985, p. 37). Facing an economic recession, the Ford administration

had an economic summit in 1974, where a consensus was reached to tackle federal deregu-

lation. The CAB became an immediate focus and a Senate hearing in 1975 brought much

economic evidence to bear that regulation had restricted pricing and entry to the detriment

of consumers. When the Carter administration came in 1976 supporting deregulation, there

was virtually full political support and the ADA was passed in 1978. In fact, the CAB

began significant reforms already by 1977, allowing “pro forma approval of discount fares”

and granting “permissive route authority, which would allow a carrier to enter and exit from

a route without CAB intervention” (Bailey et al., 1985, p. 33). The ADA specified full

deregulation by January 1, 1983, but the CAB already had granted airlines complete route

flexibility within a year of the act and new airlines were entering the market.

A third important feature is that the ultimate effects of the aviation deregulation were

uncertain and the industry responded in a number of unexpected ways. The rapidity of the

deregulation process, as described above, made the effects unlikely to be anticipated. The

expectation was that the aviation industry would become much more competitive after dereg-

ulation, leading to greater efficiencies and lower prices for air travel. The contestable market

theory, combined with the lack of evidence in support of economies of scale in the avia-

tion industry, diminished any concerns about anti-competitive effects in markets dominated

by single carriers (Borenstein, 1992). While there is significant evidence of lower general

prices and increased competition, in many ways, the actual transformations that swept the

aviation industry since deregulation had been surprising due to “mistaken expectation and

unforeseen outcomes” (Kahn, 1988). Perhaps one of the most significant yet totally unan-

ticipated responses from air carriers was the switch from operating point-to-point service to

a hub-and-spoke network. This transformation increased industry efficiency through better

capacity utilization, as captured by higher load factors per flights (Borenstein and Rose,

2011). It also allowed for more frequent departures and more flexibility in flight schedules

from hub airports, but at the cost of an increase in the average number of connections and

travel distances for itineraries originating in smaller airports. The restructuring of non-stop

destinations by airport size implied a decrease in the number of nonstop destinations at

small and some medium size communities (Dempsey, 1987; GAO, 1996), and a much larger

6

increase in air services in big cities, many of which had been selected as hub locations.15

A final feature of aviation deregulation that is fundamental to our identification strat-

egy consists of the systematic heterogeneity in the policy-induced shock to air services across

locations. The CAB undertook many efforts to ensure service to smaller communities under

regulation. In fact, the CAB deliberately set fares above cost in city-pair markets located

more than 400 miles apart (typically large, dense markets), and set fares less than the costs

in shorter city-pair markets (Bailey et al., 1985, p. 20). The loss of this cross-market subsi-

dization was a primary concern of legislators when considering deregulation, as there were

real fears that many small (and even medium-sized) communities would face substantial loss

of air service. Ultimately, arguments that commuter air services would likely take the place

of traditional airline services in these communities, as well as the institution of the Essential

Air Service program to directly subsidize air service at the smallest communities, allowed

legislators to back deregulation.16

In summary, it was well known that the price levels and route allocations set by the

CAB favored small communities. The reverse was true for large urban areas, where high

fares and the suppressed competition hindered the growth and development of air transport

services. As a result, the regulatory environment led to large and systematic deviations from

market forces.

2.2 Descriptive Evidence

The evidence for the systematic distortions in air traffic patterns across city sizes is noticeable

in the data. Figure 1 depicts the average number of air passengers per city by size category

in the years before and after the aviation deregulation, relative to the national trends.17 The

data representation shows that prior to 1978 small communities witnessed the largest increase

in air traffic – evidence that the CAB strategies of route cross-subsidization had been effective

at stimulating air service in small locations at the expense of large communities.18 Following

the Airline Deregulation Act in 1978, large urban areas benefited from a relative increase in

15The evidence on the decrease in the number of non-stop destinations at small cities as a consequenceof the aviation deregulation is mixed. While many small communities indeed suffered a net loss in non-stopdestinations reached, Morrison and Winston (1986) find that other factors specific to the post-deregulationperiod are responsible for these service losses, such as increased fuel prices, higher returns to aircraft equip-ment elsewhere, or cyclical macroeconomic conditions.

16Several very small communities did lose air traffic entirely after deregulation. However, the EssentialAir Service (EAS) program allowed many others to keep air connectivity with the nearest hub airport. Formore information on costs and benefits of the EAS Program, see the General Accounting Office (GAO, 2000)report, among others.

17For ease of representation, each time series has been rescaled by the average level of air traffic in 1977.18The small, medium and large city categories are defined by splitting the sample distribution of MSA

population levels in three equal parts, based on city size information at the beginning of the sample period.

7

the rate of air passenger growth, while small communities suffered a negative shock to the

provision of air services. These trend reversals triggered by the regime switch are consistent

with the changes in air traffic expected from the removal of capacity restrictions and price

setting schemes imposed by the CAB prior to 1978.

Going beyond average tendencies in air passenger flows by city size group, Figure 2

documents the differences in air traffic growth rates between the regulated and deregulated

periods. The scatterplots bring further support to how large and persistent was the impact

of the policy change. They also illustrate the heterogeneity across cities in air traffic changes

over time. Even though, on average, small and medium size cities have witnessed a larger

negative shock to air traffic growth than large cities, when looking within a given city size,

there is still quite a bit of variation in the magnitude of the policy-induced shock. This

aspect is going to play a crucial role in the analysis, as our estimation will exploit both

within city size and across city size variation in air traffic changes over time.

Besides the large data variation that it generates, identification from a quasi-natural

experiment requires that there are no other confounding events that also affect urban growth.

There are two major events that took place around the same time as the deregulation of the

aviation industry, which could potentially affect our ability to identify the effects induced by

the airline deregulation on regional growth.

The first event is the 1979 oil price shock, which occurred in the aftermath of the

aviation deregulation. The surge in fuel costs and energy prices in the early 1980s presum-

ably impacted not only the growth and development of the aviation industry, but also the

economy as a whole. To the extent that these shocks affected economic growth differentially

across cities of different sizes, then part of this variation may be spuriously picked up in our

estimation. However, we think this issue is less problematic for our data exercises because

our variables of interest are long-run growth rates computed over a period of time (i.e.,

1977-1991) whose start and end dates are distant from the oil crisis and its aftermath.19

A second series of events that may be of concern for our estimation strategy is the

full or partial deregulation occurring during the same time period in other industries – most

notably in the trucking and railroad industries.20 However, unlike the airline industry, we

are not aware of any evidence to suggest that these deregulation events led to systematic

heterogeneous changes in the economic activity across regions. While increased competition

and improved cost efficiency in the absence of regulation have favored industry expansion and

a rapid output growth, these trends have been observed nationwide. This implies that any

19There is no evidence in our sample that economic growth rates differ across MSA size categories,once controlling for initial conditions, industrial composition and lagged urban growth rates. Estimates areavailable upon request.

20Winston (1993) provides a comprehensive survey of the regulatory reforms implemented in the U.S. atthe end of 1970s and beginning of 1980s, including the deregulation of the transport sector.

8

systematic deviations from national trends in local economic growth rates that we identify

across different-sized communities pre- versus post- the 1978 aviation deregulation cannot

be attributed to regulatory initiatives happening simultaneously in other sectors.

3 Empirical Strategy

3.1 Basic Framework

In this paper, we take the view that aviation services are part of the local fundamentals

characterizing an urban community. As such, they contribute to enhancing local productivity

levels while, at the same time, being a valuable city amenity that enhances the quality of

life.21 In a Web Appendix22, we build these assumptions into a simple model of urban growth

to formally derive a direct relationship between changes in air traffic at the community level,

and subsequent changes in population, labor force and per-capita income. Using the notation

KiT ≡lnKi,T1

−lnKi,T0

T1−T0to denote the (log) average annual change of variable K in city i over

the time period T = [T0, T1], the resulting estimation equation can be written as follows:

YiT = βAiT +XiT0

′γ + αi + αT + εiT (1)

where Yi stands for population, employment or per-capita income in city i; Ai denotes the

level of air services in city i, as measured by the total number of enplaned passengers; the

vector XiT0 captures location-specific control variables that are observable at the beginning

of the time period T , and affect urban growth. Finally, αi and αT stand for location and

time period fixed effects, and εiT denotes the error term.

Our interest lies in estimating the effect of air services on regional growth. We expect

the coefficient of interest β to be positive and significant.23 A challenge in identifying β comes

21Several channels have been suggested for the productivity effect of air transport services. First, airtravel facilitates face-to-face communication, which is essential for innovation, technology diffusion, and forcoordination and efficient allocation of resources (see, among others, Gaspar and Glaeser, 1998; Autretschand Feldman, 1996; Hovhannisyan and Keller, 2011; Giroud, 2013). Second, the availability of air servicesreduces transaction costs, increasing the openness of a region to trade (Poole, 2010; Cristea, 2011). Thisin turn fosters labor and industrial specialization at the micro level, as well as product diversification atthe regional level, leading to increased aggregate productivity (Glaeser, Kallal, Scheinkman and Schleifer,1992; Feenstra and Kee, 2006). Finally, air traffic could raise regional productivity via agglomeration effectsand the associated positive externalities (see, among others, Rosenthal and Strange, 2004). The locationdecision of exporters and multinational firm headquarters is influenced by the quality of air services (Lovely,Rosenthal, and Sharma, 2005; Bel and Fageda, 2008). At the same time, both types of firms are shownto exert positive spillovers on local businesses, further affecting productivity (Blomstrom and Kokko, 1998;Arnold, Javorcik and Mattoo, 2011). Our intention in this study is to identify an aggregate net effect of airtraffic on urban development, working in part through any of these productivity effects.

22Web Appendix URL: http://pages.uoregon.edu/cristea/Research files/WebAppendix urban.pdf23The coefficients β, γ are reduced-form parameters whose structure is derived in the Web Appendix from

9

from the interdependency between the provision of air services and regional growth, which

raises endogeneity concerns. To address this, we take advantage of the exogenous data vari-

ation generated by the dismantling of aviation regulations. Before analyzing the approaches

we take to isolate the exogenous variation in air traffic from any endogenous responses to the

policy change, it is worth discussing some key aspects related to the specification in equation

(1).

First, the outcome variables of interest – population, per-capita income or employment

– change slowly over time. As a result, long run growth rates may be better suited for

capturing regional development. Unlike year-on-year changes, long differences of the data

are more appropriate for correctly identifying any persistent relation between the variables

of interest in the presence of significant autocorrelation (Bertrand and Duflo, 2004). For

this reason, in our estimation we consider two long-run subsample periods: one that defines

an interval of time before the aviation deregulation, and one that defines an interval of

time following the policy change. We let the time indicator T index the two time periods,

with T = 0 denoting the pre-deregulation period 1966-1977, and T = 1 denoting the post-

deregulation period 1977-1991.24 We choose the year 1977 as the cutoff point rather than

1978 – the year when the Airline Deregulation Act entered into effect – because we want to

calculate post-deregulation long-run changes in air traffic starting from a reference period

that pre-dates and thus is unaffected by any major changes in routes or price levels triggered

by the policy change.

Second, the period fixed effect αT are important for model identification as they account

for any period-specific macroeconomic factors that may influence regional growth. However,

given the long-run time horizon being considered, it is possible for cities to go through

structural changes in their urban growth paths. These changes may differ systematically

across cities depending on geographic location, economic size or industrial specialization. To

account for this in our regression model, we control for economic conditions at the beginning

of each time period T . We specify the vector XiT0 to include information on population

size, per-capita income, factor endowments, and industrial composition at the MSA level for

the base year of each long-term period. This allows us to implicitly control for things like

changes in labor productivity and skill premium across both periods, as well as across cities

of different sizes.

Finally, to account for location-specific determinants of urban growth that may be

the proposed urban growth model.24The chosen time periods are determined by the data availability on air services, and by the timing of

deregulation. In particular, year 1969 is the first year when air passenger traffic was collected and reportedat city level, while year 1991 is the earliest year that such records have become available electronically. Inthe empirical analysis, we provide evidence for the robustness of our results to perturbations in these timewindows.

10

difficult to measure, we employ city fixed effects, αi. These control for factors such as eco-

nomic fundamentals, geographical location, land area, climate, natural resource endowments,

and any persistent socio-cultural characteristics that may affect economic growth of a city.

Adding city fixed effects to the regression equation (1) allows us to remove location-specific

secular trends. A direct implication is that the model coefficients are identified from the

comparison of urban growth rates before versus after the aviation deregulation.25

This estimation approach is very demanding of the data. If the policy shock under

consideration is not large enough to generate substantial variation in the variables of interest,

or if the time period is not long enough for the economic outcomes to fully adjust to their

new equilibrium levels, then differencing the data by constructing long-run annual growth

rates, and removing location-specific trends by adding city fixed effects are going to sweep

away any useful information. Fortunately for our identification strategy, Figures 1 and 2

provide suggestive evidence of substantial variation in air passenger traffic before versus

after deregulation.

3.2 Estimation Methodology

Estimating equation (1) requires a careful consideration of the data generating process un-

derlying the changes in air traffic, AiT , in order to understand the main sources of variation

and isolate the exogenous component. We proceed by discussing first the lengths we can

go to fully exploit the quasi-natural policy experiment to identify the effects of air service

on regional growth within an OLS framework. We then describe a number of instrumental

variables strategies that we use to pin down the exogenous portion of air passenger growth,

thus allaying any remaining concerns about endogeneity.

Given the regression specification, the variation exploited for model identification

consists of the deviation in air traffic changes between the regulation (T=0) and post-

deregulation (T=1) periods, i.e., ∆AiT ≡ Ai1 − Ai0. To formally derive it, we begin by

defining the level of air traffic in city i at time t. We assume that the CAB rules imposed

during the regulatory period distorted air traffic by affecting both its level and its rate of

adjustment to local economic conditions. Thus, air traffic in city i at time t can be defined

as:

lnAit =

αi + δlnZit + αt , if deregulation (free market)

(αi + αi) + (δ + δ)lnZit + αt , if regulation(2)

where αi, δ denote the policy-induced distortions to air traffic, which can be location-specific;

25Given that the estimation sample includes two long-run periods, i.e., T ∈ {0, 1}, the city fixed effectsmodel in equation (1) delivers identical estimates as a differenced model of urban growth rates between thetwo time periods (Wooldridge, 2002, p. 284).

11

and Zit represents any city characteristics that explain the level of air traffic in city i,

including city size or income level (which are elements of Yit in equation (1)).

From equation (2), using the notation KiT ≡lnKi,T1

−lnKi,T0

T1−T0, we can write the average

annual change in air traffic during the regulatory period as:

Ai0 = α0 + (δ + δ)Zi0 (3)

where the subscript “0” indexes pre-deregulation period variables.

Similarly, we can express the average annual change in air traffic post-deregulation as:

Ai1 = α1 − αi + δZi1 − δlnZi1,T0 (4)

where the subscript “1” indexes post-deregulation period variables, and T0 denotes the initial

year of the post-deregulation period. Note that since the initial year of the post-deregulation

period coincides with the last year of the regulatory period (i.e., year 1977 in the data), in

deriving equation (4) we use policy-distorted levels of air traffic for the base year, T0, of the

post-deregulation period “1”.

The variation exploited for identifying the coefficient of interest β in equation (1) is

then given by the deviation in air traffic growth rates between pre- and post-deregulation

periods. From equations (3) and (4), we can derive this differential change as:

∆Ai ≡ Ai1 − Ai0 = ∆α− αi + δ∆Zi − δ(Zi0 + lnZi1,T0) (5)

The constant term ∆α ≡ δ1− δ0 captures the change in air traffic growth rates between the

pre- and post-deregulation periods that is uniform across all cities in the sample. The term

∆Zi captures over time changes in the growth rate of location specific determinants of air

traffic. Note that ∆Zi is different from zero only for those city characteristics that witness

systematic changes in their long-run growth rates at the time of the aviation deregulation.

This is important for model identification as many unobservable factors that jointly deter-

mine air traffic and urban growth are potentially evolving at a constant rate over the two

periods of interest T ∈ {0, 1}.Key for our purposes, equation (5) identifies the conditions under which the variation

in air traffic changes over time, i.e., ∆Ai, can be taken as exogenous with respect to the

unobserved determinants of urban growth (i.e., the residual variation in equation (1)). It also

formalizes the channels through which endogeneity may operate. The regulatory distortions

rapidly unwound by deregulation, as captured by αi and δ, add unique variation to city-

level changes in air traffic. This is the exogenous variation generated by the quasi-natural

12

experiment that we want to exploit in our estimation. However, city size or per-capita income

– the dependent variables in our baseline model – are likely part of the vector Zi. This creates

an endogeneity problem. Note that the sources of endogeneity in ∆Ai lie both in the levels

(Zi1,T0) and per-period growth rates (Zi0, Zi1) of urban growth determinants. To the extent

that we can isolate the exogenous component of the variation in ∆Ai once conditioning on

city characteristics, the OLS estimation methods should be appropriate to use in estimating

equation (1). Otherwise, we need to refer to instrumental variable methods.

At this point, before discussing each estimation method, it is useful to take a prelim-

inary look at the data and examine raw correlations between the variables in equation (5).

Table 1 provides some interesting statistics, with Panel A reporting the correlation between

city-level changes in air traffic and urban growth rates pre- versus post-deregulation (i.e.,

corr(∆Ai,∆Zi)). In general, the growth rate changes are not highly correlated among the

variables of interest, reducing concerns about simultaneity or spurious correlation. This is

in contrast to the correlation coefficients between the level of these variables – observed in

Panel B of Table 1 – where the scale differences across cities make the volume of air traffic

highly correlated with the population size and with total employment.26 Based on this, we

can reasonably argue that many of the factors that simultaneously determine air travel and

regional economic development are eliminated by double differencing the data.

Additional descriptive evidence is provided in Panel C of Table 1, which illustrates

the relatively weak correlation between the variables’ growth rates in the regulation period

(i.e., ∆Zi0) and the air traffic growth rate in the post-deregulation period (i.e., ∆Ai1).

For example, the correlation coefficient between population growth in the regulation period

and passenger growth post-deregulation is 0.37, absent any city level controls. Similarly,

the correlation between the air traffic growth rates pre- and post- deregulation is 0.33, a

value that is suggestive of some consistency in the evolution of air traffic over time, but

also of significant shocks to its growth trajectory at city level. Finally, the last row in

Panel C reports the correlations between city-level changes in air traffic growth (∆Ai) and

pre-deregulation urban growth rates (i.e., ∆Zi0). Again, we find little to no evidence of

systematic relationships – suggestive that endogeneity concerns may be limited.

3.2.1 OLS Approach

We propose several model refinements to restrict the variation in air traffic growth to only

capture the exogenous policy-induced shocks triggered by the dismantling of aviation regu-

lation.

26While the reported correlation coefficients are calculated based on data for year 1969, this pattern isfound for all other years in the sample.

13

Short post-deregulation time window. We shorten the time horizon over which

air traffic changes are measured during the post-deregulation period as a way to mitigate

the impact of reverse causality. Referring to equation (4), by limiting the time frame for

T = 1, we force the vector of city characteristics ZiT that affects Ai1 to only vary due to

immediate or short run changes. To the extent that such changes are not much different

from the changes observed the previous period – e.g., sluggish adjustments – this reduces

the reverse causality problem, increasing the chances that ∆Zi = 0 in equation (5).

In the empirical analysis, we restrict our attention to the time window 1977-1983 to

define Ai1, as this period captures the aftermath of deregulation, when the large unexpected

changes in air traffic occurred. We choose 1983 as the end-year, first, because this is when

the CAB was fully dissolved – suggestive that the industry had reached a stable equilibrium

and required no more oversight; and second, because year 1983 was relatively uneventful

at a macroeconomic level, reducing the potential for other market distortions to affect the

calculated average annual changes in air traffic.

Observed and anticipated urban growth. As equation (5) shows, the variation in

air traffic changes depends on urban growth determinants (both levels and changes). To mit-

igate endogeneity concerns, we control for these directly in our regression model. Equation

(1) already accounts for per-period initial economic conditions (lnZi1,T0), so what still needs

to be controlled for are the observed (Zi0) and anticipated (Zi1) rates of urban growth. For

that, we rely on cities’ historical population sizes. Thus, we expand our regression model by

including as additional control variables: 1) decade-long lags of city population (i.e., Li,T0−j

for j > 0); 2) per-period base year city characteristics; and, as a distinct element of the

vector of initial conditions, 3) the level of air passengers per capita at the beginning of each

period (i.e., AiT0/LiT0), as a way to account for any systematic variation in AiT across cities.

The estimating equation (1) now becomes:

YiT = βAiT +XiT0

′γ + θln(AiT0

LiT0

)+

L∑j=0

γjLi,T0−j + αi + αT + εiT (6)

with Y standing for population, employment or per-capita income in city i.

Equation (6) can achieve proper identification by OLS methods if the remaining resid-

ual variation in AiT – coming from the unwinding of policy distortions – is exogenous.

14

3.2.2 Instrumental Variables (2SLS) Approach

Differencing the data, and adding control variables and fixed effects to the regression model

may be sufficient measures to eliminate any correlation between our variable of interest and

the error term. However, in the event of remaining endogeneity concerns, we also estimate

our model using instrumental variables. For this purpose, we exploit the differential impact

that the ADA had on cities of different sizes. We instrument for the change in air traffic

growth in city i using the average change in air traffic growth over time observed across the

MSAs in the same size category as city i. Specifically, we use the interaction term between

the time period indicator T and each of the three MSA size category indicators to predict

AiT .27

Exploiting information pertaining to many cities of similar size as city i has several

advantages. It significantly reduces reverse causality concerns, since much of the data vari-

ation contained in the excluded instrument comes from sources external to city i. It also

mitigates the concern of weak instruments, as we have already documented the common

patterns among cities of similar size in their policy-induced distortions.

We also experiment with two related versions of the proposed instrument. First, within

each MSA size category, we calculate the average deviation in air traffic changes among cities

other than the particular city of focus, city i. By construction, this instrument is now purged

of any economic growth determinants specific to city i. Second, rather than rely on the

simple average of air traffic changes for cities j 6= i, we compute instead a weighted average

using the inverse of the geographic distance between cities i and j as weights. That is, we

instrument for AiT using the weighted average: 1N

∑j 6=i AjT/Distij, with city j belonging to

the same MSA size category as city i. The main benefit of using spatial weights is to place

greater importance on cities that are in proximity of city i. To the extent that the regulatory

distortions had regional components, then such a geography-weighted instrument may have

a stronger predictive power.

Besides their benefits, a potential drawback of these instruments is that, by construc-

tion, they may capture more than just the exogenous changes in air traffic growth induced

by the airline deregulation. For instance, unobserved factors that affect both air traffic and

urban growth, and that change simultaneously with the change in policy are going to load

onto the first stage estimator. To understand how large is this concern, as a preliminary

data check, we inspect for structural breaks in MSAs’ growth path around the time of the

ADA.28 The results of this exercise, relegated to the Appendix Table A1, suggest that once

27We are grateful to Jan Brueckner for suggesting these instruments.28To do so, we include the instruments as explanatory variables in a fully specified regression model of

urban growth, where the growth rate of each economic indicator used as dependent variable is calculatedover the same post-deregulation time window as the growth rate of air traffic, i.e., 1977-1983.

15

accounting for city and time fixed effects, the average annual growth rate of population,

employment or per-capita income during the period 1977-1983 does not differ from the pre-

deregulation period in a way that is systematically related to the size category of the cities

in the sample.

4 Data

The data used in this study correspond to years 1969, 1977 and 1991 (and 1983 for air

traffic), and are collected from various sources. The beginning and end years of the sample

period are dictated by data availability, as well as the distance in time away from the policy

shock represented by the 1978 ADA. The data are collected at the city or county level for

the 48 contiguous U.S. states, and aggregated to the level of metropolitan (or, in some cases

micropolitan) statistical areas.29 This seems the most appropriate spatial unit for evaluating

the economic impact of an airport’s air transport services.

The air passenger transport data are provided by the Department of Transportation

(DOT) and the Federal Aviation Administration (FAA). We collect the data from the Airport

Activity Statistics of Certificated Route Air Carriers, which cover the activity of large air

carriers certified to operate aircraft with capacity of 60 seats or more. We augment this data

with air traffic information from the Small Air Carriers Database (Form 298C Schedule T1)

provided by the DOT. We restrict attention to domestic scheduled air services and for each

city or airport in the U.S. record the total annual number of enplaned passengers. We map all

U.S. airports or city locations available in our dataset into the corresponding counties using

information from the FAA, and then map counties into MSAs based on the concordance

available from the U.S. Census Bureau.

Data on population and per-capita income is provided by the Bureau of Economic

Analysis at the county level, which we then aggregate to the MSA level.30 Employment data

- total and by major sectors (manufacturing, services, wholesale, retail, construction, trans-

portation and utilities) - are available from the County Business Patterns (CBP) provided

by the U.S. Census.31

29We use the current mapping of counties into the core based statistical areas (the majority of which areMSAs) available from the U.S. Census through their U.S Gazetteer Place data from 2006. Even if the currentdelineation does not correspond to the one implemented several decades ago, its application throughout theentire sample period ensures the consistency of statistical areas throughout the panel period.

30Nominal per-capita income rates are converted into real values using the consumer price index (CPI)series provided by the Federal Reserve Bank of St. Louis.

31For the years prior to 1986, the CBP files are not available electronically from the U.S. Census, so weuse the Inter-University Consortium for Political and Social Research (ICPSR) as our data source.

16

After combining all sources of data over the three selected years 1969, 1977 and 1991

(1983 for air traffic), and after screening the resulting sample for potential outliers, we end

up with a set of 263 urban centers.32 Each of these remaining MSAs hosts at least one

airport that has been active in every one of the three years spanning the 23-year period.

The summary statistics on the variables of interest, including the constructed annual growth

rates for the periods 1969-1977 and 1977-1991 (respectively, 1977 - 1983 for air traffic), are

reported in Table 2. An important thing to notice is the substantial variation in growth

rates over the two sample periods, especially for air passenger traffic.

5 Estimation Results

5.1 OLS Approach

Table 3 reports the results from estimating equation (6) with population growth as the local

economic outcome of interest. The first column includes city-level changes in air traffic,

in addition to a minimum set of control variables: time fixed effect and initial period con-

ditions. Population levels at the start of each time period account for differences across

cities in population growth rates, while the time fixed effect captures any post-deregulation

macroeconomic shocks that may affect the rate of population growth nationwide.

Air traffic growth has a positive effect on the rate of population growth. While signifi-

cant, this result does not take into account the possibility that prior and anticipated future

city growth are not only correlated with actual urban growth rates, but they also determine

the growth rate of air traffic post-deregulation. To remove this source of endogeneity, in

column 2 we include three decadal lags of population, in levels. The effect of air service on

population growth decreases in magnitude, but remains positive and significant.

Next, we add to the estimation model regional indicators to account for differences in

economic conditions at city level at the start of each time period. In particular, the industrial

composition of a city’s activities has been shown to be a significant determinant of urban

growth.33 Therefore, we allow the rate of urban growth to vary over time according to the

sectoral composition of cities in the initial period. The estimates are reported in column

3. As expected, differences across cities in industrial structure influence both urban growth,

32In obtaining the estimation sample, we have dropped the top and bottom 5 percent of cities based on(per-period) passenger growth rates in order to remove any outliers.

33Glaeser et al. (1992) provides evidence that inter-industry knowledge spillovers, which are facilitatedby a diverse industrial base, explain economic agglomerations. At the same time, the industrial compositionof a region also determines its average level of human capital, influencing income levels and consumption.More importantly, Brueckner (2003) provides evidence from a cross-section of MSAs that services benefitmore than manufacturing activities from the availability and quality of air services.

17

as well as the growth of air services. Once conditioning on such linkages, the impact of

air traffic changes on population growth decreases in magnitude, but remains positive and

highly significant.

Finally, there are several other location specific characteristics that need to be consid-

ered but may be difficult to measure. Some of these characteristics are time invariant, like

geographic location, natural resources, or climate. Other factors, even if location specific,

may evolve at a constant rate over time. To account for such factors that simultaneously

determine air travel and urban growth rates, we rely on city fixed effects as solution. Col-

umn 4 of Table 3 reports the results from such a fully specified regression model. This is

our preferred specification. The coefficient on the variable of interest remains positive and

significant, with a 50 percent increase in the annual air traffic growth rate leading to a 1.55

percent increase in the annual population growth rate, on average.34 Cumulating the result-

ing change in population over time, this corresponds to a 0.42 percent additional increase in

the population of the average city after a 20-year period.35

We further examine the sensitivity of our results to sample composition by verifying

whether our estimates are driven by a subset of cities, in particular, by large hub airports.

We identify the MSAs that host the largest airport hubs based on the airport classification

compiled by the Federal Aviation Administration (FAA). Column 5 reports the results from a

fully specified model estimated on a subsample that excludes large hub cities. While the effect

of air traffic on urban growth decreases in magnitude, it remains positive and significant.

This provides evidence that our finding is not a local result, driven by a particular subset of

urban centers in our sample.

Overall, the estimates in Table 3 provide robust evidence that urban growth, as mea-

sured by population size, is directly affected by the provision of air services. Next, we

investigate whether per-capita income responds in a similar manner. If air services are an

important factor into productivity changes, then a standard model of urban growth predicts

34There is a lot of variation across communities in the change in air traffic growth rates between the pre-and post-deregulation periods. The median city in the sample witnessed an average -1.9 percent annualgrowth rate in air passenger traffic in the time period following the deregulation, and this represents anegative 123 percent change relative to the average annual growth rate observed in the regulation period.To interpret our estimation coefficients, rather than rely on the large sample deviation, we chose to use amore moderate value of a 50 percent change in the air traffic growth rate. In the sample, there are 42 (16%)communities whose air traffic growth rate changed by up to 50 percent. While this may not be a very largenumber, we think this conservative scenario is more representative for out-of-sample periods. The kind ofchanges in air traffic growth rates that happened around the aviation deregulation period are probably muchlarger than what we tend to observe today.

35To arrive at this number, we take the annual population growth rate of an average city in the sample,computed over the period 1969-1991, which is equal to 1.365 per year. Then, a 50 percent increase in theair traffic growth rate predicts an annual growth rate of population of: gpredictpop = (1 + 0.0155)1.365 = 1.386.

We then apply the following calculation:Poppredict

t+20

Popactualt+20

= (1+1.386/100)20∗Popt

(1+1.365/100)20∗Popt= 1.0042 or 0.42 percent.

18

that they should directly affect changes in per-capita income as well.

Table 4 reports the estimation results following the same model specifications as in

columns 3 to 5 of Table 3. In all of the estimated regressions, the impact of air traffic on

per-capita income growth is positive and significant. The magnitude of the effects decreases

as we control for omitted variable bias by gradually accounting in our estimation for city

characteristics that simultaneously affect air traffic and urban growth rates. Based on the

preferred specification reported in column 2, a 50 percent increase in the air passenger

growth rate leads to a 1.65 percent increase in the annual growth rate of per-capita income,

on average. To put results in perspective, the annual income growth rate calculated over the

sample period is 1.76 percent for the average city. Cumulating the resulting change in per-

capita income over a 20-year period, this corresponds to a 0.57 percent additional increase

in the level of per-capita income for the average city. These effects are almost identical when

we eliminate the large hub cities from the sample (see column 3).

While there are several reasons why population growth may be preferred to employment

as an indicator of regional growth, it is customary in the urban economics literature to

investigate the effects of air traffic on regional employment. Research work has shown that

agglomerations, through the positive externalities they provide, represent a strong force

of attraction for future businesses. Furthermore, the effect of air passenger services on

employment growth may operate not only through productivity effects, but also through

quality of life considerations (i.e., urban amenities).

The results explaining the growth in employment are provided in columns 4 to 6 of Ta-

ble 4. Again, the columns report the estimates from specifications that gradually incorporate

an increasing set of control variables to explain the differences in employment growth across

communities (following the same pattern as columns 1 to 3 of the same table). Focusing on

the most complete specification reported in column 5, we find that a 50 percent increase in

the rate of regional air traffic growth leads to a 2.7 percent increase in annual employment

growth, on average. The coefficient is larger in magnitude than the effect of traffic growth on

population, suggestive of additional channels through which air services affect employment

growth that operate independently from population growth determinants. To put results

in perspective, the annual employment growth rate calculated over the sample period is 3

percent for the average city. Cumulating the resulting change in employment over a 20-year

period, this leads to an additional increase in total employment of 1.6 percent for the average

city. By dropping the large hub cities from our sample, the magnitude of the main coefficient

of interest decreases slightly but remains highly significant (column 6). This reveals that the

estimated average employment effects are not driven by a particular subgroup of cities, but

are in fact representative for the entire sample of urban centers.

19

5.2 Instrumental Variables (2SLS) Approach

To address any remaining endogeneity concerns, we experiment with two sets of excluded

instruments. We exploit information on the differences in regulatory distortions across cities

based on their size. Table 5 reports the 2SLS estimation results for each of the three urban

growth indicators as dependent variable. For conciseness, we only report the coefficients for

the variable of interest, however the estimated model includes the complete set of controls

and fixed effects used in prior estimations.

Columns 1 to 4 of Table 5 include the 2SLS estimates for the specification explaining

urban population growth. We first instrument for the difference in the growth rate of air

traffic over time using the average air traffic change observed across cities within the same

size category. That is, we use the interaction terms between the post-deregulation time

indicator and city size indicators, distinguishing between small, medium or large cities. The

results are reported in column 1. In the subsequent columns, we experiment with variants

of the proposed instrument. In column 2 we use as instrument the average air traffic change

observed in other cities within the same size category as the city in question. In column 3

we use proximity as criteria to weight the other cities whose air traffic changes are used in

predicting a city’s response to deregulation. If regulatory distortions have regional-specific

components, we are able to exploit that additional source of exogenous variation with this

location-specific instrument. In column 4, we combine the predictive power of the last two

instruments by using them together in the same regression.

Across all four 2SLS specification, the effect of air traffic on population growth remains

positive and statistically significant. Using the coefficient from column 1, a 50 percent

increase in the air passenger growth rate leads to a 4.15 percent increase in the annual

rate of population growth, on average. Comparing columns 1 to 4, there are no notable

differences between the estimated coefficient of interest. However, the IV estimates are

larger in magnitude when compared to the OLS results from column 4 in Table 3. While

this direction of change may seem contrary to the expected positive correlation between air

traffic and population growth rates, this outcome is not new to the literature (e.g., Duranton

and Turner, 2012; Duranton et al., 2014). Like others have pointed out, transportation

infrastructure might get allocated disproportionately to cities that are less productive, poorer

and that grow slower, which explains the underlying negative correlation. Put differently,

unobservable factors causing urban growth during the regulation period may have determined

the CAB to inefficiently allocate air services across communities, oversupplying the cities

witnessing slower growth at the expense of rapidly growing areas. In addition, much of the

positive correlation between air traffic and urban growth is already accounted for by the

control variables and the regression fixed effects. Lastly, it is also possible that air traffic

20

changes are measured or recorded with error at city level, inducing attenuation bias in the

OLS estimates.

Two conditions are necessary for these proposed variables to qualify as valid instru-

ments. First, the excluded instruments need to be correlated with city level changes in air

traffic growth, conditional on all the right-hand side regression variables. In that respect,

both the anecdotal evidence and the data representation in Figure 2 seem to give support

to this condition. Second, the excluded instruments must not be correlated with the resid-

ual from the urban growth regression. Here, it is important to emphasize that the model

specification directly controls for the city size at the beginning of each period, as well as for

the city’s history of economic growth, in addition to accounting for secular trends using city

fixed effects. This removes concerns about omitted variable bias, minimizing the ways in

which the excluded instruments could be correlated with the urban growth residual.

Formal tests for the validity of the excluded instruments are reported at the bottom

of Table 5. From the first stage coefficients, it appears that the excluded instruments are

jointly significant in explaining changes over time in air traffic growth. The reported F-

statistic and the partial R-squared are large, being well above the conventional critical levels.

Furthermore, the overidentification test for the exogeneity of the excluded instruments also

provides support for their choice. Based on the reported Hansen J-statistic, the test fails

to reject the hypothesis that the instruments are uncorrelated with the residual from the

population growth regression (once conditioning on all the control variables and fixed effects).

Moving to the specifications explaining per-capita income growth, we report the corre-

sponding 2SLS estimates in columns 5 to 8 of Table 5. We exploit the same set of excluded

instruments as before (in columns 1 to 4), and we find a similar pattern of results. The effect

of air traffic on per-capita income growth is positive and significant in all of the 2SLS esti-

mations, and the magnitude of the coefficient is almost double in size compared to the OLS

result (reported in Table 4). Based on the estimate from column 5, a 50 percent increase in

the air passenger growth rate leads to a 3.2 percent increase in the annual rate of per-capita

income growth, on average. Since the estimated regression model is no different from the one

explaining population growth, the first stage coefficients reported at the bottom of columns

5 to 8 are identical to the ones in columns 1 to 4. Only the test of overidentifying restrictions

provides different statistics due to a change in the dependent variable. Judging from the

reported Hansen J statistic, the chosen instruments are exogenous to the regression model,

being orthogonal to the residual income growth rates.

Finally, columns 9-12 of Table 5 report the 2SLS estimates for total employment as

measure of urban growth. The pattern of results observed so far applies to this specification

as well. Again, we find that the 2SLS estimates for the effect of air traffic growth are positive

21

and significant, with a magnitude that is almost twice the size of the corresponding OLS

coefficient from column 5 in Table 4. The same reasoning for this finding that we provided

earlier applies here as well. Based on the estimate reported in column 9, a 50 percent

increase in the air passenger growth rate leads to a 5.5 percent increase in the annual rate

of employment, on average. The reported tests of overidentifying restrictions validate once

again the excluded instruments’ exogeneity.

To summarize the results so far, we have pursued a variety of estimation strategies to

identify a causal effect of the growth in air traffic on the economic development and long-run

growth of U.S. cities, and found surprisingly consistent and robust evidence for a positive

and significant effect.

5.3 Robustness Exercises

This section describes additional data exercises that bring support to the results of the paper.

The aim here is two-fold. First, we want to shed some light on the channels through which

air traffic changes affect urban growth. For that reason, we focus attention on employment

composition effects, as well as on the expansion in the number of local businesses. Second,

we want to verify the robustness of our findings to the choice of time windows, as well as to

the use of the quasi-natural experiment as a source of exogenous data variation.

5.3.1 Sectoral Decomposition and Firm Extensive Margin

Studies analyzing the impact of road infrastructure find robust evidence that retail and

wholesale industries benefit the most from improved market access and lower transportation

costs (Michaels, 2008). Similarly, studies focusing on air transport have shown that service

sectors have been the most responsive to the availability of air services (Brueckner, 2003).

Following this practice, we exploit the sectoral disaggregation available in the city level

employment data to investigate the sectors whose labor demand is sensitive to changes in

air passenger transport.

The results reported in Table 6 are consistent with the prior literature. It seems that

the total employment effects that we documented earlier are mainly driven by employment

growth in service and trade-related industries. This finding is consistent across OLS and

2SLS specifications (where the latter are estimated using average deregulation shocks by

city size category as instruments).36

36This finding gives support to early reports by the CAB suggesting that market towns – where tradingis the most important occupation – and diversified cities – which have a significant representation of gov-ernment, finance or professional activities – represent the type of cities that generate the largest demand forair traffic (Sealy, 1968, p. 149; Eads, 1972).

22

A second robustness exercise examines the impact of air traffic changes on the establish-

ment of new businesses in a location. While we found direct evidence of employment growth

at city level, it is not clear whether this is caused by production growth among existing firms

(i.e., intensive margin effect), or by the entry of new firms (i.e., extensive margin effect).

This distinction may be important to policy makers as business agglomerations are known

to generate positive spillovers (e.g., Baldwin and Martin, 2004). Using information on the

number of businesses operating in a location, which is available from the County Business

Patterns database, we estimate the same fully specified regression model as before and report

the estimates in Table 7. Columns 1 and 2 provide OLS estimates on a sample with and

without large city hubs. Columns 3 and 4 report 2SLS estimates using the average growth

in air traffic across cities of similar size category as instruments. Across all estimations, air

traffic changes have a positive and significant effect on the growth in the number of local

businesses. Depending on specification, a 50 percent increase in the air traffic growth rate

leads to an increase in the growth of the number of firms between 2 and 6.75 percent. Once

again, we find that the 2SLS estimates are larger in magnitude than the OLS counterparts,

which may be the result of either attenuation bias from measurement error in firm count, or,

more likely, from a disproportionate allocation of air services to slow growing cities during

the CAB regulatory period.

5.3.2 Coefficient Sensitivity to Variations in Time Windows

One consideration regarding our econometric strategy is that the time periods over which

the growth rates are calculated may have a direct impact on the data variation used for

model identification. This is true especially if macroeconomic factors have a differential

impact across cities in the years defining the time window under consideration, case in

which the time fixed effect is not able to account for this heterogeneity. For instance, in

constructing the long-run urban growth rates, we have chosen year 1991 as the end-point of

the post-deregulation period. However, year 1991 is a recession year. This could attenuate

the long-run growth rates used for model identification, becoming particularly problematic

if the recession had a more negative effect on small or medium size cities compared to large

cities (potentially leading to spurious correlation).

To assess the robustness of our findings, we evaluate the sensitivity of our estimates to

changes in the time window defining the post-deregulation period.37 Table 8 povides the re-

sults, reporting only the coefficient for the air traffic growth rate estimated both by OLS and

2SLS methods. We consider four time periods as alternatives to the baseline case, and report

37Since the regression model controls for initial economic conditions (i.e., base year city characteristics),the starting periods are less of a concern for influencing the econometric analysis and the estimates.

23

the coefficients for each distinct time period by row. The first two alternative time periods

correspond to deviations from the end-year 1991 defining the post-deregulation period over

which the urban growth indicators are calculated. The next two period redefinitions corre-

spond to deviations from end-year 1983, defining the period over which air traffic growth

rates are calculated. Comparing the estimates by column across all rows, it becomes clear

that our findings are not sensitive to the time window being considered. All the estimates

are positive and significant, being reasonably close in magnitude to our baseline results.

5.3.3 Placebo Test

To verify the robustness of our results to the identification mechanism – which exploits the

large and permanent policy shock of the 1978 aviation deregulation – we develop a placebo

test based on an arbitrary deregulation year. Focusing on a period with no major policy

changes affecting air services, such as the post-deregulation period 1983-1994, we select

year 1987 as a hypothetical deregulation year and define the time period 1983-1987 as the

“regulatory” period, and the time period 1987-1991 as the “aftermath” of the hypothetical

policy change. To maintain the same pattern in the construction of the data sample as before,

we allow the urban growth rates to be calculated over a longer “post-deregulation” period,

i.e., 1987-1994. Using these re-defined time windows, which exploit a purely hypothetical

policy shock, we estimate the same regression model relating the differential changes in air

traffic and urban growth rates, i.e., equation (6).

We report the results of our placebo experiment in Table 9. If the identification method

correctly isolates the policy-induced variation in air traffic changes across cities, then this

method should fail to detect any significant variation using this counterfactual data sample.

The estimates reported in Table 9 seem to confirm this intuition. The OLS results from

columns 1-3 show that deviations in air traffic changes around year 1987 have no effect

on either population, income or employment growth rates. The fact that the placebo test

delivers zero coefficients for the variable of interest is informative also because it suggests that

our data differencing strategy is able to successfully deal with the simultaneity between air

traffic and urban growth.38 While we cannot implement instrumental variables estimations

given the lack of variation in our variable of interest, we can report the coefficients from the

first stage regressions. Using the average changes in air traffic across cities within the same

size category as excluded instruments, the results reported in columns 4 and 5 suggest that

the instruments have no predictive power in explaining a city’s air traffic growth rate. Note

38While air traffic growth should have a direct effect on urban growth and vice-versa during the deregulatedtime period 1983-1994, in the absence of any major policy shocks to air traffic, the regression variables loosemost of their predictive power when taking a difference in growth rates around the arbitrary year 1978(especially in the presence of other control variables and fixed effects).

24

that these are the same type of instruments that have been successfully used in previous IV

estimations, when exploiting actual policy-induced changes in air traffic growth.

6 Conclusion

Public spending on aviation at the federal, state, and local levels have constantly increased

since the beginning of commercial aviation, reaching ten percent of total public spending on

infrastructure by 2004 (CBO, 2007). To evaluate the benefits of such resource allocations, it

is crucial that we understand the implications of these investments for regional development

and economic growth. Surprisingly, this research question has received little attention in the

empirical literature, to a large extent because of the difficulty in going beyond correlations

to identify actual causation.

This paper exploits the quasi-natural experiment created by the signing of the 1978 Air-

line Deregulation Act to identify the link between airline traffic and local economic growth.

Our findings suggest that exogenous increases in air services lead to statistically and econom-

ically significant increases in regional growth. For example, increasing the annual growth

rate of air passenger traffic by 50 percent for a given city leads to an increase in the rate of

population growth of 1.55 percent (conservative OLS estimates). Cumulating the estimated

effect over a 20-year period, this corresponds to an additional 0.42 percent increase in the

level of population (we get slightly larger magnitudes with respect to air service effects on

per-capita income and employment growth).

From these estimates, one can do simple calculations about how much a region may

gain in additional income from increased air service. For example, an average city that

witnesses a 50 percent increase in the air traffic growth rate will gain a stream of income

over a 20-year period, which in discounted present value terms corresponds to an average

7.4 percent increase in its total real GDP in 1978.39 This estimate is equivalent to a total

discounted present value of 523.3 million dollars in 1978 for the average city.

Our analysis finds only small differences in the main results across communities based

on their average size. We also find evidence that shifts in industrial composition are associ-

ated with a growth in the aviation networks. When estimating the employment effects by

sector, we find that service and retail industries are the ones experiencing the significant

growth effects.

These findings are important for better understanding the determinants of regional

growth, but also for influencing policies designed to allocate public infrastructure spending.

39This estimate corresponds to the stream of income generated over a two decade period, and combinesthe effects of air service on population growth and per-capita income growth over that same time interval.The calculation is done based on the more conservative OLS estimates.

25

We note that the identification strategy we use forces us to focus on a period of time when

commercial aviation, while it witnessed dramatic growth, might not have been as essential

to consumers and businesses as it is today. This suggests that the importance of air service

for regional growth may be even greater today than our estimated effects.

References

[1] Aschauer, David, 1989. Is Public Expenditure Productive? Journal of Monetary Eco-nomics 23, 177-200.

[2] Arnold, Jens, Beata S. Javorcik and Aaditya Mattoo, 2011. Does Services LiberalizationBenefit Manufacturing Firms? Evidence from the Czech Republic. Journal of Interna-tional Economics, forthcoming.

[3] Audretsch, David B. and Maryann P. Feldman, 1996. R&D Spillovers and the Geographyof Innovation and Production. American Economic Review 86(3), 630-640.

[4] Bailey, Elizabeth E., David R. Graham and Daniel P. Kaplan, 1985. Deregulating theAirlines, Cambridge, Mass.: MIT Press.

[5] Baldwin, Richard E., and Philippe Martin, 2004. Agglomeration and regional growth.Handbook of Regional and Urban Economics 4, 2671-2711.

[6] Banerjee, Abhijit, Esther Duflo, and Nancy Qian, 2012. On the Road: Access to Trans-portation Infrastructure and Economic Growth in China. NBER working paper 17897.

[7] Bel, Germa and Xavier Fageda, 2008. Getting There Fast: Globalization, Intercontinen-tal Flights and Location of Headquarters. Journal of Economic Geography 8, 471-495.

[8] Bertrand, Marianne and Esther Duflo, 2004. How Much Should We Trust Differences-in-Differences Estimates? Quarterly Journal of Economics 119(1), 249-275.

[9] Blomstrom, Magnus and Ari Kokko, 1998. Multinational Corporations and Spillovers.Journal of Economic Surveys 12(3), 247-277.

[10] Borenstein, Severin, 1992. The Evolution of the U.S. Airline Competition. Journal ofEconomic Perspectives 6(2), 45-73.

[11] Borenstein, Severin and Nancy Rose, 2011. How Airline Markets Work ... Or Do They?Regulatory Reform in the Airline Industry. In Economic Regulation and Its Reform:What Have We Learned?, N.L. Rose ed., University of Chicago Press.

[12] Brueckner, Jan, 2003. Airline Traffic and Urban Economic Development. Urban Studies40(8), 1455-1469.

26

[13] Chandra, Amitabh and Eric Thompson, 2000. Does Public Infrastructure Affect Eco-nomic Activity? Evidence from the Rural Interstate Highway System. Regional Scienceand Urban Economics 30, 457-490.

[14] Congressional Budget Office (CBO), 2007. Trends in Public Spending on Transporta-tion and Water Infrastructure, 1956 to 2004. Available at:www.cbo.gov/sites/default/files/cbofiles/ftpdocs/85xx/doc8517/08-08-infrastructure.pdf

[15] Cristea, Anca, 2011. Buyer-Seller Relations in International Trade: Evidence from U.S.States’ Exports and Business-class Travel. Journal of International Economics 84(2),207- 220.

[16] Dempsey, Paul Stephen, 1987. Dark Side of Deregulation: Its Impact on Small Com-munities. Administrative Law Review 39, 445-465.

[17] Donaldson, Dave, 2010. Railroads of the Raj: Estimating the Impact of TransportationInfrastructure. Massachusetts Institute of Technology, mimeo.

[18] Donaldson, Dave, and Richard Hornbeck, 2013. Railroads and American EconomicGrowth: A Market Access Approach. NBER working paper 19213.

[19] Duranton, Gilles and Matthew Turner, 2012. Urban Growth and Transportation. Reviewof Economic Studies 79(4), 1407-1440..

[20] Duranton, Gilles, Peter Morrow and Matthew Turner, 2014. Roads and Trade: Evidencefrom the U.S.. Review of Economic Studies, forthcoming.

[21] Eads, George, 1972. The Local Service Airline Experiment, the Brookings Institution,Washington, D.C..

[22] Evans, Paul and Georgios Karras, 1994. Are Government Activities Productive? Evi-dence From a Panel of U.S. States. The Review of Economics and Statistics 76, 1-11.

[23] Faber, Benjamin, 2013. Trade Integration, Market Size, And Industrialization: EvidenceFrom Chinas National Trunk Highway System. UC Berkley, mimeo.

[24] Feenstra, Robert and Hiau Looi Kee, 2008. Export Variety and Country Productiv-ity: Estimating the Monopolistic Competition Model with Endogenous Productivity.Journal of International Economics 74(2), 500-518.

[25] Fernald, John, 1999. Roads to Prosperity? Assessing the Link Between Public Capitaland Productivity. American Economic Review 89, 619-638.

[26] Gaspar, Jess and Edward L. Glaeser, 1998. Information Technology and the Future ofCities. Journal of Urban Economics 43(1), 136-156.

[27] General Accounting Office (GAO), 1996. Airline Deregulation: Changes in Airfares,Service, and Safety at Small, Medium-Sized, and Large Communities, GAO/RCED-96-79.

27

[28] General Accounting Office (GAO), 2000. Essential Air Service: Changes in SubsidyLevels, Air Carrier Costs, and Passenger Traffic, GAO/RCED-00-34.

[29] Giroud, Xavier, 2013. Proximity and Investment: Evidence from Plan Level Data. TheQuarterly Journal of Economics 128(2), 861-915.

[30] Glaeser, Edward L., Jose Scheinkman and Andrei Schleifer, 1995. Economic Growth ina Cross-Section of Cities. Journal of Monetary Economics 36, 117-143.

[31] Glaeser, Edward L., Heidi Kallal, Jose Scheinkman and Andrei Schleifer, 1992. Growthin Cities. Journal of Political Economy 100(6), 1126-1152.

[32] Green, Richard K., 2007. Airports and Economic Development. Real Estate Economics35, 91-112.

[33] Hanson, Gordon, 2005. Market Potential, Increasing Returns, and Geographic Concen-tration. Journal of International Economics 67, 1-24.

[34] Head, Keith and Thierry Mayer, 2006. Regional Wage and Employment Responses toMarket Potential in the EU. Regional Science and Urban Economics 36, 573-594.

[35] Holtz-Eakin, Douglas, 1994. Public-Sector Capital and the Productivity Puzzle. Reviewof Economics and Statistics 76(1), 12-21.

[36] Hovhannisyan, Nune and Wolfgang Keller, 2011. International Business Travel: AnEngine of Innovation? NBER Working Paper No. 17100.

[37] Kahn, Alfred E., 1988. Surprises of Airline Deregulation. American Economic ReviewPapers and Proceedings, 316-322.

[38] Lovely, Mary E., Rosenthal Stuart S. and Shalini Sharma, 2005. Information, Agglom-eration and the Headquarters of U.S. Exporters. Regional Science and Urban Economics35, 167-191.

[39] Michaels, Guy, 2008. The Effect of Trade on the Demand for Skill: Evidence from theInterstate Highway System. The Review of Economics and Statistics 90(4), 683-701.

[40] Morrison, S. A., and C. Winston, 1986. The Economic Effects of Airline Deregulation.Washington, D.C.: The Brookings Institution.

[41] Munnell, Alicia H., 1992. Policy Watch: Infrastructure Investment and EconomicGrowth. Journal of Economic Perspectives 6(4), 189-198.

[42] Poole, Jennifer, 2010. Business Travel as an Input to International Trade. University ofCalifornia Santa Cruz, mimeo.

[43] Redding, Stephen and Anthony Venables, 2004. Economic Geography and InternationalInequality. Journal of International Economics 62, 53-82.

28

[44] Redding, Stephen, Daniel Sturm, and Nikolaus Wolf, 2011. History and Industry Loca-tion: Evidence From German Airports. The Review of Economics and Statistics 93(3),814-831.

[45] Rosenthal Stuart and William C. Strange, 2004. Evidence on the Nature and Sourcesof Agglomeration Economies. Handbook of Regional and Urban Economics Vol. 4, 2119-2171.

[46] Sealy, Kenneth R., 1968. The Geography of Air Transport, Aldine Publishing Company,Chicago, IL.

[47] Sheard, Nicholas, 2012. The Effect of Air Transport on the Production of Goods andServices. Stockholm University, mimeo.

[48] Shirley, Chad and Clifford Winston, 2004. Firm Inventory Behavior and the Returnsfrom Highway Infrastructure Investments. Journal of Urban Economics 55, 398-415.

[49] Trefler, Daniel, 2004. The Long and Short of the Canada-U.S. Free Trade Agreement.American Economic Review 94(4), 870-895.

[50] Winston, Clifford, 1993. Economic Deregulation: Days of Reckoning for Microe-conomists. Journal of Economic Literature 31(3), 1263-1289.

[51] Wooldridge, Jeffery M., 2002. Econometric Analysis of Cross Section and Panel Data.Cambridge, MA: MIT Press.

29

-.3-.2

-.10

.1.2

.3.4

1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991

Small MSA Medium MSA Large MSA

Log

Aver

age

Air P

asse

nger

Tra

ffic

(nor

mal

ized

)

Source: Authors’ Calculations

Figure 1: Trends in Air Passengers by City Size Category

Note: The series represent the average (log) number of air passengers by year and city size category. Toremove level differences in air traffic and facilitate relative comparisons between city size categories, eachseries has been adjusted by its average value for year 1977. The small, medium and large city categories aredefined by splitting the sample distribution of MSA population levels in three equal parts, based on city sizeinformation at the beginning of the sample period.

30

 

-20

-15

-10

-50

510

15

8 10 12 14 16

City Size

Period 1969 - 1977

Avg.

Ann

ual C

hang

e in

Tra

vele

rs p

er C

apita

(1969 MSA Population)

-20

-15

-10

-50

510

15

8 10 12 14 16

City Size

Period 1977 - 1983

Avg.

Ann

ual C

hang

e in

Tra

vele

rs p

er C

apita

(1969 MSA Population)

Source: Authors’ Calculations

Figure 2: City-Level Changes in Air Traffic per Capita During Regulation and Deregulation

Note: The y-axis of each scatterplot represents the average annual percentage change in air passengers percapita at city level. The average percentage change is calculated over two distinct periods: 1969-1977, whichdenotes the pre-deregulation period; and 1977-1983, which corresponds to the aftermath of the aviationderegulation. The pattern of changes in air traffic per capita differs quite substantially across the two timeperiods, suggestive of the major shock to the aviation industry induced by the policy change. The fact thatdifferences in air traffic changes display significant variation both within and across city size groups can beindicative of the heterogeneous effect of deregulation across locations.

31

Tab

le1:

Corr

ela

tion

Coeffi

cie

nts

Pan

el

A:

Corr

ela

tion

Coeffi

cie

nts

betw

een

Ch

an

ges

inV

ari

ab

les’

Gro

wth

Rate

s

∆P

ass

enger

∆P

op

ula

tion

∆In

com

e∆

Em

plo

ym

ent

Gro

wth

Gro

wth

Gro

wth

Gro

wth

∆P

asse

nge

rG

row

th(l

og)

1.0

0∆

Pop

ula

tion

Gro

wth

(log)

0.2

31.0

0∆

Inco

me

Gro

wth

(log)

0.4

00.1

61.0

00

∆E

mp

loym

ent

Gro

wth

(log)

0.2

90.4

90.5

91.0

0

Not

e:E

ach

vari

able

mea

suri

ng

ach

an

ge

ingro

wth

rate

sis

const

ruct

edat

MS

Ai

leve

las

foll

ows:

∆y i

=1 14log( y i,19

91

yi,1

977

) −1 8log( y i,19

77

yi,1

969

) ,w

ith

y∈{p

ass

enger

s;p

erca

pit

ain

com

e;em

plo

ym

ent}

.

Pan

el

B:

Corr

ela

tion

Coeffi

cie

nts

betw

een

(log)

Base

Year

Vari

ab

les

Pass

enger

sP

op

ula

tion

Inco

me

Em

plo

ym

ent

1969

1969

1969

1969

Pas

sen

gers

1969

1.0

0P

opu

lati

on

1969

0.8

81.0

0In

com

e19

69

0.5

00.4

81.0

0E

mp

loym

ent

1969

0.8

70.9

80.5

11.0

0

Pan

el

C:

Corr

ela

tion

Coeffi

cie

nts

betw

een

Vari

ab

les’

Gro

wth

Rate

sacro

ssP

eri

od

s

Pas

sen

ger

Pop

ula

tion

Inco

me

Em

plo

ym

ent

Pass

enger

Pop

ula

tion

Inco

me

Em

plo

ym

ent

Gro

wth

69-7

7G

row

th69-7

7G

row

th69-7

7G

row

th69-7

7G

row

th77-8

3G

row

th77-9

1G

row

th77-9

1G

row

th77-9

1P

asse

nge

rG

row

th69

-77

1.00

Pop

ula

tion

Gro

wth

69-7

70.

501.0

0In

com

eG

row

th69

-77

0.16

0.0

11.0

0E

mp

loym

ent

Gro

wth

69-7

70.

440.7

20.3

61.0

0P

asse

nge

rG

row

th77

-83

0.33

0.3

7-0

.20

0.2

11.0

0P

opu

lati

onG

row

th77

-91

0.39

0.7

5-0

.13

0.5

50.4

61.0

0In

com

eG

row

th77

-91

0.04

0.1

6-0

.30

-0.0

10.3

70.2

01.0

0E

mp

loym

ent

Gro

wth

77-9

10.

350.6

6-0

.16

0.5

20.4

90.8

50.4

71.0

0∆

Pas

sen

ger

Gro

wth

(91-

77)

-(7

7-69

)-0

.23

0.0

9-0

.30

-0.0

30.8

40.2

70.3

60.3

0

32

Table 2: Summary Statistics

Variable (log) Obs Mean Std. Dev. Min Max

Growth rates: total samplePassenger Growth 526 0.012 0.061 -0.185 0.150Population Growth 526 0.012 0.012 -0.014 0.060Income Growth 526 0.017 0.011 -0.010 0.051Employment Growth 526 0.028 0.018 -0.033 0.080

Growth rates: Pre-Deregulation PeriodPassenger Growth ’69-’77 263 0.049 0.034 -0.040 0.150Population Growth ’69-’77 263 0.015 0.012 -0.005 0.053Income Growth ’69-’77 263 0.025 0.008 0.000 0.051Employment Growth ’69-’77 263 0.031 0.020 -0.033 0.080

Growth rates: Post-Deregulation PeriodPassenger Growth ’77-’83 263 -0.025 0.060 -0.185 0.108Population Growth ’77-’91 263 0.009 0.011 -0.014 0.060Income Growth ’77-’91 263 0.010 0.006 -0.010 0.029Employment Growth ’77-’91 263 0.025 0.015 -0.014 0.074

Base Year Variables (per long-run period)a

Passengers per-capita 526 -0.713 0.937 -3.456 2.036Population 526 12.034 1.273 9.273 16.020Income per-capita 526 9.220 0.184 8.478 9.746Employment 526 10.687 1.390 7.745 14.990Share Manufacturing 526 -1.430 0.604 -3.870 -0.350Share Services 526 -1.632 0.270 -2.722 -0.646Share Wholesale 526 -2.731 0.366 -5.573 -1.675Share Retail 526 -1.482 0.227 -2.341 -0.862Share Transport/Utilities 526 -2.793 0.320 -3.799 -1.397Share Construction 524 -2.800 0.359 -3.878 -1.266Population Lag(T0−10)

b 526 11.933 1.254 8.870 15.940Population Lag(T0−20)

b 526 11.795 1.222 8.648 15.764Population Lag(T0−30)

b 526 11.609 1.211 8.665 15.567a Initial conditions refer to economic indicators from year 1969 for the pre-deregulation period,1969-1977, and from year 1977 for the post-deregulation period 1977-1991.b For the pre-deregulation period, the three lags of population levels correspond to years 1960,1950 and 1940. For the post-deregulation period, the three lags of population levels correspondto years 1969, 1960 and 1950.

33

Table 3: OLS Effect of Air Travel Changes on Population Growth

Dependent Variable: Population Growth RateiT

Basic Population Industrial MSA XXNo HubsLags Composition Fixed Effects

(1) (2) (3) (4) (5)

Passenger Growth RateiT 0.108** 0.084** 0.066** 0.031** 0.026**[0.010] [0.010] [0.009] [0.007] [0.007]

Passenger per capitaT00.002* 0.001 -0.001 0.005** 0.004**[0.001] [0.001] [0.001] [0.002] [0.001]

PopulationT0 -0.002** 0.015* 0.009 -0.053** -0.053**[0.000] [0.007] [0.006] [0.007] [0.007]

Population LagT0−10 -0.007 -0.008 0.002 0.001[0.006] [0.005] [0.003] [0.003]

Population LagT0−20 0.003 -0.005 0.004 0.004[0.005] [0.004] [0.003] [0.003]

Population LagT0−30 -0.014** -0.010** 0.011** 0.010*[0.003] [0.002] [0.004] [0.004]

Income per capitaT0-0.021** -0.012 -0.010

[0.004] [0.008] [0.008]EmploymentT0

0.013** 0.001 0.001[0.003] [0.005] [0.005]

Share ManufacturingT0 0.000 0.002 0.002[0.001] [0.002] [0.002]

Share ServicesT00.004 0.003 0.003

[0.003] [0.004] [0.004]Share RetailT0 0.008* -0.001 -0.001

[0.004] [0.005] [0.005]Share WholesaleT0

-0.001 0.002* 0.002*[0.001] [0.001] [0.001]

Share Transport/UtilitiesT00.002 -0.004* -0.005*

[0.001] [0.002] [0.002]Share ConstructionT0 0.005** 0.001 0.001

[0.001] [0.001] [0.002]

Time Fixed Effect yes yes yes yes yesMSA Fixed Effects no no no yes yesLarge Hubs Included? yes yes yes yes no

Observations 526 526 524 524 486R-squared 0.301 0.479 0.577 0.673 0.693∗∗p < 0.01, ∗p < 0.05, +p < 0.1. Robust standard errors clustered at MSA level in brackets.

Notes: The reported results correspond to the baseline regression equation (6). The data panel includes two long-run timeperiods, 1969-1977 and 1977-1991, defined around the year of the aviation deregulation. The dependent variable, i.e., annualpopulation growth, is calculated at MSA level over each time period. The main variable of interest, i.e., air passenger annualgrowth rate, is calculated over a shorter post-deregulation period (1977-1983) to better isolate the exogenous variation inducedby the policy shock. The decennial population lags control for previous and anticipated city growth rates. The period-specific initial economic conditions help to mitigate endogeneity. The time period and city level fixed effects account for bothmacroeconomic and for location-specific secular growth trends. The large city hubs (dropped in the last column) are identifiedbased on a classification provided by the Federal Aviation Administration.

34

Table 4: OLS Effect of Air Travel Changes on Local Per-Capita Income Growth andon Employment Growth

Dependent Variable: Annual Growth RateiT for...

Per Capita Income EmploymentIndustrial MSA XNo Hubs Industrial MSA XNo Hubs

Composition Fixed Effects Composition Fixed Effects(1) (2) (3) (4) (5) (6)

Passenger Growth RateiT 0.039** 0.033** 0.034** 0.100** 0.054** 0.050**[0.007] [0.008] [0.009] [0.014] [0.013] [0.014]

Passenger per capitaT0-0.000 0.003+ 0.003+ -0.000 0.007* 0.006*[0.000] [0.002] [0.002] [0.001] [0.003] [0.003]

Income per capitaT0 -0.023** -0.143** -0.144** -0.030** -0.034** -0.030*[0.004] [0.007] [0.008] [0.006] [0.013] [0.012]

PopulationT0-0.005 0.014 0.013 0.010* 0.064** 0.065**[0.004] [0.009] [0.009] [0.004] [0.012] [0.013]

Population LagT0−10 0.006 -0.001 -0.001 -0.001 0.005 0.004[0.005] [0.002] [0.003] [0.004] [0.005] [0.004]

Population LagT0−20 -0.004 -0.003 -0.003 -0.014* -0.000 -0.001[0.005] [0.003] [0.003] [0.006] [0.004] [0.004]

Population LagT0−30 0.002 -0.004 -0.003 -0.006 0.006 0.004[0.003] [0.003] [0.004] [0.004] [0.005] [0.005]

EmploymentT00.001 -0.000 0.000 0.009* -0.106** -0.106**

[0.002] [0.007] [0.007] [0.004] [0.010] [0.010]Share ManufacturingT0 -0.001 0.000 0.001 0.000 -0.004 -0.003

[0.001] [0.002] [0.002] [0.002] [0.003] [0.003]Share ServicesT0

0.002 -0.000 -0.000 0.012** -0.007 -0.007[0.002] [0.004] [0.004] [0.003] [0.010] [0.010]

Share RetailT0-0.002 0.002 0.003 0.026** 0.005 0.006[0.003] [0.006] [0.006] [0.005] [0.008] [0.008]

Share WholesaleT0 -0.000 0.002 0.002 0.002 0.004* 0.004*[0.001] [0.001] [0.001] [0.002] [0.002] [0.002]

Share Transport/UtilitiesT00.001 -0.000 -0.000 0.002 -0.007* -0.007*

[0.001] [0.002] [0.002] [0.002] [0.003] [0.003]Share ConstructionT0

-0.000 -0.006** -0.005** 0.003+ -0.004 -0.003[0.001] [0.002] [0.002] [0.002] [0.003] [0.003]

Time Fixed Effect yes yes yes yes yes yesMSA Fixed Effects no yes yes no yes yesLarge Hubs Included? yes yes no yes yes no

Observations 524 524 486 524 524 486R-squared 0.628 0.918 0.920 0.586 0.755 0.763∗∗p < 0.01, ∗p < 0.05, +p < 0.1. Robust standard errors clustered at MSA level in brackets.

Notes: The reported results correspond to the baseline regression equation (6). The data panel includes two long-run timeperiods, 1969-1977 and 1977-1991, defined around the year of the aviation deregulation. The dependent variable, i.e., annualper-capita income growth, is calculated at MSA level over each time period. The main variable of interest, i.e., air passengerannual growth rate, is calculated over a shorter post-deregulation period (1977-1983) to better isolate the exogenous variationinduced by the policy shock. The decennial population lags control for previous and anticipated city growth rates. Theperiod-specific initial economic conditions help to mitigate endogeneity. The time period and city level fixed effects accountfor both macroeconomic and for location-specific secular growth trends. The large city hubs (dropped in the last column) areidentified based on a classification provided by the Federal Aviation Administration.

35

Tab

le5:

Inst

rum

enta

lV

ari

ab

les

Est

imate

sfo

rth

eE

ffect

of

Air

Tra

vel

on

Urb

an

Gro

wth

Dep

en

dent

Vari

ab

le:

An

nu

al

Gro

wth

Rate

iT

Popu

lati

on

Inco

me

Em

plo

ym

en

t(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)(9

)(1

0)(1

1)

(12)

Pas

sen

ger

Gro

wth

Rat

e iT

0.08

3**

0.08

6**

0.08

1**

0.0

84**

0.0

64**

0.06

6**

0.07

2**

0.06

9**

0.101

**0.1

04*

*0.0

79*

0.0

94*

*[0

.017

][0

.018

][0

.021

][0

.017

][0

.017

][0

.018

][0

.020

][0

.016]

[0.0

25]

[0.0

27]

[0.0

31]

[0.0

25]

Tim

eF

ixed

Eff

ect

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

MS

AF

ixed

Eff

ects

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

Init

ial

Eco

nom

icC

ond

itio

ns

(T0)

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

Pop

.L

ags

(T0−10,T

0−20,T

0−30)

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

Sec

tora

lC

om

pos

itio

nye

sye

syes

yes

yes

yes

yes

yes

yes

yes

yes

yes

Ob

serv

atio

ns

520

520

520

520

520

520

520

520

520

520

520

520

R-s

q0.

602

0.59

30.

608

0.59

90.

912

0.9

110.9

080.9

100.7

400.7

38

0.7

500.7

44

Exclu

ded

Inst

rum

en

ts:

Pos

t-D

ereg

ula

tion

Per

iod

0.01

4+0.

014+

0.0

14+

×M

ediu

mC

ity

[0.0

08]

[0.0

08]

[0.0

08]

Pos

t-D

ereg

ula

tion

Per

iod

0.05

8**

0.0

58**

0.058

**×

Larg

eC

ity

[0.0

09]

[0.0

09]

[0.0

09]

Avg.

Pas

sen

ger

Gro

wth

in0.8

32**

0.57

7**

0.83

2**

0.577

**0.8

32**

0.5

77*

*oth

erC

itie

sby

Siz

e[0

.115

][0

.160

][0

.115

][0

.160

][0

.115

][0

.160]

Avg.

Pas

sen

ger

Gro

wth

in6.

315*

*3.3

49*

6.3

15**

3.34

9*

6.3

15**

3.3

49*

oth

erC

itie

sby

Loca

tion

&S

ize

[1.0

28]

[1.3

59]

[1.0

28]

[1.3

59]

[1.0

28]

[1.3

59]

Fir

stS

tage

Sta

tist

ics:

F-s

tati

stic

30.1

152

.39

37.7

533

.47

30.1

152.

3937.

7533.

4730.

1152.3

937.

7533.4

7P

arti

alR

-squ

ared

0.17

30.

156

0.13

30.

179

0.17

30.1

560.1

330.1

790.1

730.1

56

0.1

330.1

79

Han

sen

Jst

atis

tic

0.0

92n

.a.

n.a

.0.

081

2.40

3n

.a.

n.a

.0.

092

1.0

84n

.a.

n.a

.0.8

67

Han

sen

Jp

-val

ue

0.7

62n

.a.

n.a

.0.

777

0.12

1n

.a.

n.a

.0.

762

0.2

98n

.a.

n.a

.0.3

52

∗∗p<

0.0

1,∗ p<

0.05

,+p<

0.1.

Rob

ust

stan

dar

der

rors

clu

ster

edat

MS

Ale

vel

inb

rack

ets.

Notes:

Th

ere

port

edre

sult

sco

rres

pon

dto

the

regre

ssio

neq

uati

on

(6)

esti

mate

dby

inst

rum

enta

lvari

ab

les

met

hod

s(2

SL

S).

We

inst

rum

ent

for

the

vari

ab

leof

inte

rest

–air

pass

enger

an

nu

al

gro

wth

rate

–u

sin

gth

efo

llow

ing

excl

ud

edin

stru

men

ts:

1)

ati

me

du

mm

yfo

rth

ep

ost

-der

egu

lati

on

per

iod

,allow

edto

vary

by

city

size

cate

gory

(th

ism

easu

reco

rres

pon

ds

toth

ed

evia

tion

inair

traffi

cch

an

ges

over

tim

eob

serv

edam

on

gci

ties

wit

hin

the

sam

esi

zegro

up

s);

2)

the

aver

age

air

traffi

cgro

wth

rate

ofother

citi

esw

ith

inth

esa

me

size

cate

gory

;3).

the

aver

age

air

traffi

cgro

wth

rate

ofother

citi

esw

ith

inth

esa

me

size

cate

gory

,w

eighte

dby

dis

tan

ce.

Bec

au

seof

the

pre

sen

ceof

city

fixed

effec

ts,

the

vari

ati

on

exp

loit

edby

the

last

two

inst

rum

ents

als

oco

nsi

sts

of

dev

iati

on

sin

air

traffi

cch

an

ges

over

tim

e.T

he

com

ple

telist

of

the

contr

ols

an

dfi

xed

effec

tsfr

om

pri

or

esti

mati

on

sare

incl

ud

edin

all

the

rep

ort

edsp

ecifi

cati

on

s.

36

Tab

le6:

Eff

ect

of

Air

Tra

vel

Ch

an

ges

on

Local

Em

plo

ym

ent

Gro

wth

by

Secto

r

Dep

en

dent

Vari

ab

le:

Secto

rE

mp

loym

ent

Gro

wth

Rate

iT

Man

ufa

ctu

rin

gS

erv

ices

Whole

sale

Reta

il(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)(9

)(1

0)

(11)

(12)

XO

LS

2S

LS

2S

LS

XO

LS

2S

LS

2S

LS

XO

LS

2S

LS

2S

LS

XO

LS

2S

LS

2S

LS

Pas

sen

ger

Gro

wth

Rat

e iT

-0.0

11-0

.049

-0.0

92

0.0

71**

0.2

03**

0.2

12**

0.0

11

0.0

87+

0.0

65

0.0

40**

0.0

80**

0.0

78**

[0.0

27]

[0.0

55]

[0.0

59]

[0.0

17]

[0.0

38]

[0.0

38]

[0.0

24]

[0.0

50]

[0.0

53]

[0.0

11]

[0.0

25]

[0.0

25]

Tim

eF

ixed

Eff

ect

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

MS

AF

ixed

Eff

ects

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

Init

ial

Eco

nom

icC

ond

itio

ns

(T0)

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

Pop

.L

ags (

T0−10,T

0−20,T

0−30)

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

Sec

tora

lC

omp

osit

ion

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

Ob

serv

atio

ns

519

512

512

524

520

520

523

520

520

524

520

520

R-s

qu

ared

0.78

90.7

88

0.7

81

0.7

02

0.6

20

0.6

08

0.9

02

0.897

0.8

99

0.8

12

0.8

01

0.8

02

Exclu

ded

Inst

rum

en

ts:

Pos

t-D

ereg

.P

erio

d×

Med

ium

Cit

yye

sye

sye

sye

sP

ost-

Der

eg.

Per

iod×

Lar

geC

ity

yes

yes

yes

yes

Pax

Gro

wth

inoth

erC

itie

sby

Siz

eye

sye

sye

sye

sP

axG

row

thin

oth

erC

itie

sby

Loc.

&S

ize

yes

yes

yes

yes

Fir

stS

tage

Sta

tsis

tics:

F-s

tat

28.6

431.4

630.1

133.4

730

.11

33.4

730.1

133.4

7H

anse

nJ

stat

0.1

40

2.0

14

7.6

58

0.0

18

0.001

1.6

01

1.0

74

0.3

27

Han

sen

Jp

-val

0.7

09

0.1

56

0.0

06

0.8

94

0.985

0.2

06

0.3

00

0.5

68

∗∗p<

0.0

1,∗ p<

0.05

,+p<

0.1.

Rob

ust

stan

dar

der

rors

clu

ster

edat

MS

Ale

vel

inb

rack

ets.

Notes:

Th

ere

port

edre

sult

sco

rres

pon

dto

the

base

lin

ere

gre

ssio

neq

uati

on

(6),

wit

hth

em

od

ifica

tion

that

sect

or

level

emp

loym

ent

gro

wth

rate

sare

use

das

dep

end

ent

vari

ab

les.

Th

ed

ata

pan

elin

clu

des

two

lon

g-r

un

tim

ep

erio

ds,

1969-1

977

an

d1977-1

991,

defi

ned

aro

un

dth

eyea

rof

the

avia

tion

der

egu

lati

on

.T

he

dep

end

ent

vari

ab

le,

i.e.

,an

nu

al

gro

wth

inse

ctor

level

emp

loym

ent,

isca

lcu

late

dat

MS

Ale

vel

over

each

tim

ep

erio

d.

Th

em

ain

vari

ab

leof

inte

rest

,i.e.

,air

pass

enger

an

nu

al

gro

wth

rate

,is

calc

ula

ted

over

the

short

erp

ost

-der

egu

lati

on

per

iod

(1977-1

983)

tob

ette

ris

ola

teth

eex

ogen

ou

svari

ati

on

ind

uce

dby

the

policy

shock

.T

ore

move

end

ogen

eity

con

cern

s,in

the

rep

ort

ed2S

LS

esti

mate

sin

colu

mn

s2,

5,

8an

d11,

the

gro

wth

rate

of

air

traffi

cis

inst

rum

ente

dby

the

sam

eex

clu

ded

vari

ab

les

as

those

rep

ort

edin

colu

mn

1of

tab

le5.

Fu

rth

er,

inth

ere

port

ed2S

LS

inco

lum

ns

3,

6,

9an

d12,

the

gro

wth

rate

of

air

traffi

cis

inst

rum

ente

dby

the

sam

eex

clu

ded

vari

ab

les

as

those

rep

ort

edin

colu

mn

4of

tab

le5.

Th

efi

rst

stage

regre

ssio

nle

ad

sto

the

sam

ees

tim

ate

sas

pre

vio

usl

yre

port

ed.

Th

eco

mp

lete

list

of

the

contr

ols

an

dfi

xed

effec

tsfr

om

pri

or

esti

mati

on

sare

incl

ud

edin

all

the

rep

ort

edsp

ecifi

cati

on

s.

37

Table 7: Effect of Air Travel Changes on the Number of Local Businesses

Dependent Variable: Growth Rate in Number of FirmsiTXXXXOLS XXXXXXOLS XXXXXX2SLS 2SLS

Basic No Hubs City Category City Category(1) (2) (3) (4)

Passenger Growth RateiT 0.039** 0.035** 0.123** 0.135**[0.010] [0.011] [0.032] [0.033]

Large Hubs Included? yes no yes yesTime Fixed Effect yes yes yes yesMSA Fixed Effects yes yes yes yesInitial Economic Conditions (T0) yes yes yes yesPop. Lags(T0−10,T0−20,T0−30) yes yes yes yesSectoral Composition yes yes yes yes

Observations 524 486 520 520R-squared 0.832 0.841 0.769 0.750

Excluded Instruments:

Post-Deregulation Period × Medium City 0.013+[0.008]

Post-Deregulation Period × Large City 0.046**[0.009]

Avg. Passenger Growth in 0.444**other Cities by Size [0.156]

Avg. Passenger Growth in 2.628+other Cities by Location & Size [1.347]

First Stage Statistics:F-statistic 16.59 17.80Partial R-squared 0.100 0.103Hansen J statistic 0.034 0.114Hansen J p-value 0.854 0.736∗∗p < 0.01, ∗p < 0.05, +p < 0.1. Robust standard errors clustered at MSA level in brackets.

Notes: The reported results correspond to the baseline regression equation (6). The data panel includes two long-run timeperiods, 1969-1977 and 1977-1991, defined around the year of the aviation deregulation. The dependent variable, i.e., averageannual change in the number of firms, is calculated at MSA level over each time period. The main variable of interest, i.e.,air passenger annual growth rate, is calculated over the shorter post-deregulation period (1977-1983) to better isolate theexogenous variation induced by the policy shock. The decennial population lags control for previous and anticipated citygrowth rates. The period-specific initial economic conditions help to mitigate endogeneity. The time period and city fixedeffects account for both macroeconomic and location-specific secular growth trends. The large city hubs (dropped in the lastcolumn) are identified based on a classification provided by the Federal Aviation Administration.

38

Tab

le8:

Rob

ust

ness

:S

en

siti

vit

yof

Est

imate

sto

Red

efi

nin

gth

eP

ost

-Dere

gu

lati

on

Tim

eP

eri

od

s

Dep

en

dent

Vari

ab

le:

An

nu

al

Gro

wth

Rate

for.

..P

opu

lati

on

Inco

me

Em

plo

ym

en

tX

OL

SX

2S

LS

X2S

LS

XO

LS

X2S

LS

X2S

LS

XO

LS

X2S

LS

X2S

LS

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Base

lin

eE

stim

ate

s:U

rban

Gro

wth

Win

dow

=[1

977−

1991

]&

XX

XP

oli

cyS

hoc

kW

indow

X=

[197

7−

1983

]0.0

31**

0.0

83**

0.0

84**

0.0

33**

0.0

64**

0.0

69**

0.0

54**

0.1

01**

0.0

94**

[0.0

07]

[0.0

17]

[0.0

17]

[0.0

08]

[0.0

17]

[0.0

16]

[0.0

13]

[0.0

25]

[0.0

25]

Sen

siti

vit

yto

Lon

g-R

un

Win

dow

for

Urb

an

Gro

wth

:

Urb

an

Gro

wth

Win

dow

=[1

977−

1989

]0.0

32**

0.0

81**

0.0

83**

0.0

37**

0.0

84**

0.0

89**

0.0

64**

0.1

30**

0.1

37**

[0.0

07]

[0.0

17]

[0.0

17]

[0.0

10]

[0.0

21]

[0.0

20]

[0.0

16]

[0.0

31]

[0.0

32]

Urb

an

Gro

wth

Win

dow

=[1

977−

1994

]0.0

31**

0.0

76**

0.0

76**

0.0

27**

0.0

55**

0.0

56**

0.0

50**

0.0

71**

0.0

65**

[0.0

07]

[0.0

16]

[0.0

16]

[0.0

07]

[0.0

15]

[0.0

14]

[0.0

11]

[0.0

22]

[0.0

21]

Sen

siti

vit

yto

Short

-Ru

nW

indow

for

Air

Tra

ffic

Gro

wth

:

Poli

cyS

hoc

kW

indow

=[1

977−

1982

]0.0

27**

0.0

78**

0.0

88**

0.0

20**

0.0

56**

0.0

64**

0.0

42**

0.0

91**

0.1

02**

[0.0

07]

[0.0

17]

[0.0

18]

[0.0

07]

[0.0

18]

[0.0

18]

[0.0

12]

[0.0

27]

[0.0

27]

Poli

cyS

hoc

kW

indow

=[1

977−

1984

]0.0

38**

0.0

80**

0.0

81**

0.0

26**

0.0

48**

0.0

52**

0.0

50**

0.0

91**

0.0

87**

[0.0

07]

[0.0

15]

[0.0

15]

[0.0

07]

[0.0

16]

[0.0

14]

[0.0

12]

[0.0

25]

[0.0

23]

MS

AF

ixed

Eff

ects

yes

yes

yes

yes

yes

yes

yes

yes

yes

Tim

eF

ixed

Eff

ect

yes

yes

yes

yes

yes

yes

yes

yes

yes

Bas

eY

ear

Con

trol

sye

sye

sye

sye

sye

syes

yes

yes

yes

Pop

.L

ags (

T0−10,T

0−20,T

0−30)

yes

yes

yes

yes

yes

yes

yes

yes

yes

Sec

tora

lC

omp

osit

ion

yes

yes

yes

yes

yes

yes

yes

yes

yes

Exclu

ded

Inst

rum

en

ts:

Pos

t-D

ereg

ula

tion

Per

iod×

Med

ium

Cit

yye

sye

sye

sP

ost-

Der

egu

lati

onP

erio

d×

Lar

geC

ity

yes

yes

yes

Pax

Gro

wth

inoth

erC

itie

sby

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esti

mate

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39

Table 9: Placebo Test: 1987 as Deregulation Year over the Period 1983-1994

Dependent Variable: Annual Growth RateiT for ... Air Passenger GrowthiT

Population X Income Employm.OLS OLS OLS XX1st Stage 1st Stage(1) (2) (3) (4) (5)

Passenger Growth RateiT 0.009 0.008 0.014[0.006] [0.005] [0.011]

Excluded Instruments:Post-Deregulation Period 0.012

× Medium City [0.023]Post-Deregulation Period 0.020

× Large City [0.029]Avg. Passenger Growth in -0.045

other Cities by Size [0.326]

Avg. Passenger Growth in -2.495other Cities by Location & Size [2.309]

First Stage F-statistic 0.23 1.06

Time Fixed Effect yes yes yes yes yesMSA Fixed Effects yes yes yes yes yesInitial Economic Conditions (T0) yes yes yes yes yesPop. Lags (T0−10,T0−20,T0−30) yes yes yes yes yesSectoral Composition yes yes yes yes yes

Observations 503 503 503 500 500R-squared 0.565 0.891 0.829 0.748 0.750∗∗p < 0.01, ∗p < 0.05, +p < 0.1. Robust standard errors clustered at MSA level in brackets.

Notes: The reported results are the outcome of a placebo experiment defined over the period 1983-1994. Year 1987 is setas the time of a hypothetical aviation deregulation episode, such that the period 1983-1987 becomes the “pre-deregulation”period, and 1987-1994 becomes the “post-deregulation” period. We further limit the “post-deregulation” adjustment period forair traffic growth to the time window 1987-1991. The reported estimates in columns 1-3 correspond to the baseline regressionequation (6), estimated using the redefined pre- versus post-deregulation periods. The reported estimates in columns 4-5correspond to the first stage estimates for air traffic growth. The set of excluded instruments are the same as reported incolumn 1, respectively 4 of Table 5. The complete list of the controls and fixed effects from prior estimations are included inall the reported specifications.

40

Appendix Tables

Table A1: The Effect of City Size Group on Short-run Urban Growth Rates

Annual Growth RateiT

XX Population XXIncome per-capita XXXEmployment(1) (2) (3)

Post-Deregulation Period × Med. City -0.001 0.002 -0.001[0.002] [0.002] [0.003]

Post-Deregulation Period × Lg. City -0.001 0.001 -0.003[0.002] [0.002] [0.004]

Passenger Growth RateiT 0.026+ 0.048∗∗ 0.097∗∗

[0.013] [0.013] [0.027]Passenger per capitaT0

0.003 0.002 0.008[0.002] [0.003] [0.005]

PopulationT0 -0.028∗ 0.028∗ 0.119∗∗

[0.012] [0.012] [0.021]Population LagT0−10 0.001 -0.003 0.003

[0.002] [0.004] [0.004]Population LagT0−20 0.010∗ 0.008 0.021∗

[0.005] [0.005] [0.010]Population LagT0−30 0.010* -0.007 0.004

[0.004] [0.005] [0.008]Income per capitaT0

0.034∗∗ -0.142∗∗ 0.019[0.011] [0.014] [0.022]

EmploymentT0-0.004 -0.002 -0.130∗∗

[0.009] [0.009] [0.016]Share ManufacturingT0 0.009∗∗ 0.009∗∗ 0.010+

[0.003] [0.003] [0.006]Share ServicesT0

0.007 -0.001 -0.005[0.006] [0.006] [0.012]

Share RetailT0-0.001 0.002 -0.013[0.009] [0.008] [0.017]

Share WholesaleT0 0.005∗ 0.002 0.011∗∗

[0.002] [0.002] [0.003]Share Transport/UtilitiesT0

-0.001 0.002 -0.005[0.003] [0.004] [0.007]

Share ConstructionT00.005∗ -0.003 -0.002[0.002] [0.003] [0.005]

Time Fixed Effect yes yes yesMSA Fixed Effects yes yes yes

Observations 524 524 524R-squared 0.351 0.888 0.583∗∗p < 0.01, ∗p < 0.05, +p < 0.1. Robust standard errors clustered at MSA level in brackets.

Notes: The reported results correspond to the baseline regression equation (6). The difference from prior estimations is thatthe two time periods considered here are 1969-1977 and 1977-1983, i.e., the same time periods over which the growth rate inair traffic is constructed. The main goal of these exercises is to see whether there are systematic changes in urban growthrates over the two time periods by city size category, once accounting for all the control variables and fixed effects previouslyconsidered. Since the interaction terms between the Post-Deregulation dummy and MSA size category indicators are consideredas exogenous instruments for the change in the growth rate of air traffic post-deregulation, it is crucial that they are not affectingsimultaneously the urban economic growth indicators of interest.

41